diff --git a/.gitattributes b/.gitattributes index 9d1e8f814..fb5f5f453 100644 --- a/.gitattributes +++ b/.gitattributes @@ -4,3 +4,5 @@ *.gpickle filter=lfs diff=lfs merge=lfs -text *.xlsx filter=lfs diff=lfs merge=lfs -text *.npy filter=lfs diff=lfs merge=lfs -text +src/modules/applications/qml/classification/data/Images_2D/test_data/Negative/Negative.zip filter=lfs diff=lfs merge=lfs -text +src/modules/applications/qml/classification/data/Images_2D/test_data/Positive/Positive.zip filter=lfs diff=lfs merge=lfs -text diff --git a/.github/workflows/container_build_publish.yml b/.github/workflows/container_build_publish.yml index f87c80782..de2229f2a 100644 --- a/.github/workflows/container_build_publish.yml +++ b/.github/workflows/container_build_publish.yml @@ -20,7 +20,9 @@ jobs: runs-on: ubuntu-latest strategy: matrix: - platform: [linux/amd64, linux/arm64] + platform: [linux/amd64] + # platform: [linux/amd64,linux/arm64] + # ARM builds are (temporarily) removed in release 2.1.5 because pyscipopt 5.0 is unavailable for this platform # Sets the permissions granted to the `GITHUB_TOKEN` for the actions in this job. permissions: contents: read diff --git a/.gitignore b/.gitignore index 682f2c7ac..030fa7b8f 100644 --- a/.gitignore +++ b/.gitignore @@ -317,3 +317,5 @@ $RECYCLE.BIN/ /.settings/envs/ /configs/ /docs/_autosummary/ +src/modules/applications/qml/classification/data/Images_2D/test_data/**/*.jpg +src/modules/applications/qml/classification/data/Images_2D/**/embeddings.csv diff --git a/.settings/module_db.json b/.settings/module_db.json index 5c449d8ae..d1004fb96 100644 --- a/.settings/module_db.json +++ b/.settings/module_db.json @@ -1,3608 +1,4293 @@ -{ - "build_number": 18, - "build_date": "22-01-2025 13:49:05", - "git_revision_number": "c817270e589cd85c29fa8644cd14eeafde2215f1", - "modules": [ - { - "name": "PVC", - "class": "PVC", - "module": "modules.applications.optimization.PVC.PVC", - "submodules": [ - { - "name": "Ising", - "class": "Ising", - 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"modules.applications.qml.generative_modeling.training.qcbm", + "requirements": [ + { + "name": "numpy", + "version": "1.26.4" + }, + { + "name": "cma", + "version": "4.0.0" + }, + { + "name": "matplotlib", + "version": "3.9.3" + }, + { + "name": "tensorboard", + "version": "2.18.0" + }, + { + "name": "tensorboardX", + "version": "2.6.2.2" + } + ], + "submodules": [] + }, + { + "name": "Inference", + "class": "Inference", + "args": {}, + "module": "modules.applications.qml.generative_modeling.training.inference", + "requirements": [ + { + "name": "numpy", + "version": "1.26.4" + } + ], + "submodules": [] + } + ] + }, + { + "name": "PresetQiskitNoisyBackend", + "class": "PresetQiskitNoisyBackend", + "args": {}, + "module": "modules.applications.qml.generative_modeling.mappings.preset_qiskit_noisy_backend", + "requirements": [ + { + "name": "qiskit", + "version": "1.3.0" + }, + { + "name": "qiskit_ibm_runtime", + "version": "0.33.2" + }, + { + "name": "qiskit_aer", + "version": "0.15.1" + }, + { + "name": "numpy", + "version": "1.26.4" + } + ], + "submodules": [ + { + "name": "QCBM", + "class": "QCBM", + "args": {}, + "module": "modules.applications.qml.generative_modeling.training.qcbm", + "requirements": [ + { + "name": "numpy", + "version": "1.26.4" + }, + { + "name": "cma", + "version": "4.0.0" + }, + { + "name": "matplotlib", + "version": "3.9.3" + }, + { + "name": "tensorboard", + "version": "2.18.0" + }, + { + "name": "tensorboardX", + "version": "2.6.2.2" + } + ], + "submodules": [] + }, + { + "name": "Inference", + "class": "Inference", + "args": {}, + "module": "modules.applications.qml.generative_modeling.training.inference", + "requirements": [ + { + "name": "numpy", + "version": "1.26.4" + } + ], + "submodules": [] + } + ] + } + ] + } + ] + } + ] + }, + { + "name": "Discrete Data", + "class": "DiscreteData", + "args": {}, + "module": "modules.applications.qml.generative_modeling.data.data_handler.discrete_data", + "requirements": [ + { + "name": "numpy", + "version": "1.26.4" + } + ], + "submodules": [ + { + "name": "CircuitCardinality", + "class": "CircuitCardinality", + "args": {}, + "module": "modules.applications.qml.generative_modeling.circuits.circuit_cardinality", + "requirements": [], + "submodules": [ + { + "name": "LibraryQiskit", + "class": "LibraryQiskit", + "args": {}, + "module": "modules.applications.qml.generative_modeling.mappings.library_qiskit", + "requirements": [ + { + "name": "qiskit", + "version": "1.3.0" + }, + { + "name": "numpy", + "version": "1.26.4" + }, + { + "name": "qiskit_aer", + "version": "0.15.1" + } + ], + "submodules": [ + { + "name": "QCBM", + "class": "QCBM", + "args": {}, + "module": "modules.applications.qml.generative_modeling.training.qcbm", + "requirements": [ + { + "name": "numpy", + "version": "1.26.4" + }, + { + "name": "cma", + "version": "4.0.0" + }, + { + "name": "matplotlib", + "version": "3.9.3" + }, + { + "name": "tensorboard", + "version": "2.18.0" + }, + { + "name": "tensorboardX", + "version": "2.6.2.2" + } + ], + "submodules": [] + }, + { + "name": "QGAN", + "class": "QGAN", + "args": {}, + "module": "modules.applications.qml.generative_modeling.training.qgan", + "requirements": [ + { + "name": "numpy", + "version": "1.26.4" + }, + { + "name": "torch", + "version": "2.2.2" + }, + { + "name": "matplotlib", + "version": "3.9.3" + }, + { + "name": "tensorboard", + "version": "2.18.0" + }, + { + "name": "tensorboardX", + "version": "2.6.2.2" + } + ], + "submodules": [] + }, + { + "name": "Inference", + "class": "Inference", + "args": {}, + "module": "modules.applications.qml.generative_modeling.training.inference", + "requirements": [ + { + "name": "numpy", + "version": "1.26.4" + } + ], + "submodules": [] + } + ] + }, + { + "name": "LibraryPennylane", + "class": "LibraryPennylane", + "args": {}, + "module": "modules.applications.qml.generative_modeling.mappings.library_pennylane", + "requirements": [ + { + "name": "pennylane", + "version": "0.39.0" + }, + { + "name": "pennylane-lightning", + "version": "0.39.0" + }, + { + "name": "numpy", + "version": "1.26.4" + }, + { + "name": "jax", + "version": "0.4.35" + }, + { + "name": "jaxlib", + "version": "0.4.35" + } + ], + "submodules": [ + { + "name": "QCBM", + "class": "QCBM", + "args": {}, + "module": "modules.applications.qml.generative_modeling.training.qcbm", + "requirements": [ + { + "name": "numpy", + "version": "1.26.4" + }, + { + "name": "cma", + "version": "4.0.0" + }, + { + "name": "matplotlib", + "version": "3.9.3" + }, + { + "name": "tensorboard", + "version": "2.18.0" + }, + { + "name": "tensorboardX", + "version": "2.6.2.2" + } + ], + "submodules": [] + }, + { + "name": "QGAN", + "class": "QGAN", + "args": {}, + "module": "modules.applications.qml.generative_modeling.training.qgan", + "requirements": [ + { + "name": "numpy", + "version": "1.26.4" + }, + { + "name": "torch", + "version": "2.2.2" + }, + { + "name": "matplotlib", + "version": "3.9.3" + }, + { + "name": "tensorboard", + "version": "2.18.0" + }, + { + "name": "tensorboardX", + "version": "2.6.2.2" + } + ], + "submodules": [] + }, + { + "name": "Inference", + "class": "Inference", + "args": {}, + "module": "modules.applications.qml.generative_modeling.training.inference", + "requirements": [ + { + "name": "numpy", + "version": "1.26.4" + } + ], + "submodules": [] + } + ] + }, + { + "name": "CustomQiskitNoisyBackend", + "class": "CustomQiskitNoisyBackend", + "args": {}, + "module": "modules.applications.qml.generative_modeling.mappings.custom_qiskit_noisy_backend", + "requirements": [ + { + "name": "qiskit", + "version": "1.3.0" + }, + { + "name": "qiskit_aer", + "version": "0.15.1" + }, + { + "name": "numpy", + "version": "1.26.4" + } + ], + "submodules": [ + { + "name": "QCBM", + "class": "QCBM", + "args": {}, + "module": "modules.applications.qml.generative_modeling.training.qcbm", + "requirements": [ + { + "name": "numpy", + "version": "1.26.4" + }, + { + "name": "cma", + "version": "4.0.0" + }, + { + "name": "matplotlib", + "version": "3.9.3" + }, + { + "name": "tensorboard", + "version": "2.18.0" + }, + { + "name": "tensorboardX", + "version": "2.6.2.2" + } + ], + "submodules": [] + }, + { + "name": "Inference", + "class": "Inference", + "args": {}, + "module": "modules.applications.qml.generative_modeling.training.inference", + "requirements": [ + { + "name": "numpy", + "version": "1.26.4" + } + ], + "submodules": [] + } + ] + }, + { + "name": "PresetQiskitNoisyBackend", + "class": "PresetQiskitNoisyBackend", + "args": {}, + "module": "modules.applications.qml.generative_modeling.mappings.preset_qiskit_noisy_backend", + "requirements": [ + { + "name": "qiskit", + "version": "1.3.0" + }, + { + "name": "qiskit_ibm_runtime", + "version": "0.33.2" + }, + { + "name": "qiskit_aer", + "version": "0.15.1" + }, + { + "name": "numpy", + "version": "1.26.4" + } + ], + "submodules": [ + { + "name": "QCBM", + "class": "QCBM", + "args": {}, + "module": "modules.applications.qml.generative_modeling.training.qcbm", + "requirements": [ + { + "name": "numpy", + "version": "1.26.4" + }, + { + "name": "cma", + "version": "4.0.0" + }, + { + "name": "matplotlib", + "version": "3.9.3" + }, + { + "name": "tensorboard", + "version": "2.18.0" + }, + { + "name": "tensorboardX", + "version": "2.6.2.2" + } + ], + "submodules": [] + }, + { + "name": "Inference", + "class": "Inference", + "args": {}, + "module": "modules.applications.qml.generative_modeling.training.inference", + "requirements": [ + { + "name": "numpy", + "version": "1.26.4" + } + ], + "submodules": [] + } + ] + } + ] + } + ] + } + ], + "requirements": [] + }, + { + "name": "Classification", + "class": "Classification", + "module": "modules.applications.qml.classification.classification", + "submodules": [ + { + "name": "Image Data", + "class": "ImageData", + "args": {}, + "module": "modules.applications.qml.classification.data.data_handler.image_data", + "requirements": [ + { + "name": "pandas", + "version": "2.2.3" + }, + { + "name": "pillow", + "version": "11.1.0" + }, + { + "name": "torch", + "version": "2.2.2" + }, + { + "name": "torchvision", + "version": "0.17.2" + }, + { + "name": "tqdm", + "version": "4.67.1" + }, + { + "name": "numpy", + "version": "1.26.4" + }, + { + "name": "scikit-learn", + "version": "1.4.2" + }, + { + "name": "qiskit_aer", + "version": "0.15.1" + }, + { + "name": "tensorboard", + "version": "2.18.0" + } + ], + "submodules": [ + { + "name": "Hybrid", + "class": "Hybrid", + "args": {}, + "module": "modules.applications.qml.classification.training.hybrid", + "requirements": [ + { + "name": "matplotlib", + "version": "3.9.3" + }, + { + "name": "torch", + "version": "2.2.2" + }, + { + "name": "tqdm", + "version": "4.67.1" + }, + { + "name": "numpy", + "version": "1.26.4" + }, + { + "name": "tensorboardX", + "version": "2.6.2.2" + } + ], + "submodules": [] + } + ] + } + ], + "requirements": [ + { + "name": "numpy", + "version": "1.26.4" + }, + { + "name": "torch", + "version": "2.2.2" + }, + { + "name": "pennylane", + "version": "0.39.0" + }, + { + "name": "qiskit", + "version": "1.3.0" + }, + { + "name": "qiskit-machine-learning", + "version": "0.8.2" + }, + { + "name": "torchvision", + "version": "0.17.2" + } + ] + } + ] +} \ No newline at end of file diff --git a/.settings/requirements_full.txt b/.settings/requirements_full.txt index 3dbe2043d..fe49be018 100644 --- a/.settings/requirements_full.txt +++ b/.settings/requirements_full.txt @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:d4c3173f52d75a21ffdab2d0bf6ad86f6bca7374ec6cae23a420d4dfc90d9162 -size 738 +oid sha256:973cb037747cb907a88b41e71c606d0788c011276ab6c680233681964230cfe9 +size 947 diff --git a/AUTHORS b/AUTHORS index 9397a0e5f..d7123791c 100644 --- a/AUTHORS +++ b/AUTHORS @@ -2,11 +2,19 @@ # # This does not necessarily list everyone who has contributed code, # but focuses on main contributors. -Philipp Ross (BMW Group) -Marvin Erdmann (BMW Group) -Andre Luckow (BMW Group) -Greshma Shaji (BMW Group) -Florian Kiwit (BMW Group) +Philipp Ross (BMW AG) +Marvin Erdmann (BMW AG) +Andre Luckow (BMW AG) +Greshma Shaji (BMW AG) +Florian Kiwit (BMW AG) +Benjamin Decker (BMW AG) Jürgen Schwitalla (Eviden) Chris van den Oetelaar (Capgemini) Niklas Steinmann (Fraunhofer FOKUS) +Florian Geissler (Fraunhofer IKS) +Theodora-Augustina Dragan (Fraunhofer IKS) +Eric Stopfer (Fraunhofer IIS) +Kimberly Lange (OptWare GmbH) +Johannes Oberreuter (Machine Learning Reply GmbH) +Zao Chen (Machine Learning Reply GmbH) +Alessandro Farace (Machine Learning Reply GmbH) diff --git a/Dockerfile b/Dockerfile index c4b7ca484..16c0b7dc3 100644 --- a/Dockerfile +++ b/Dockerfile @@ -1,6 +1,6 @@ FROM python:3.12 RUN pip install --upgrade pip COPY . . -RUN pip install --default-timeout=3600 -r .settings/requirements_full.txt +RUN pip install --default-timeout=7200 -r .settings/requirements_full.txt ENTRYPOINT ["python", "src/main.py"] diff --git a/NOTICE b/NOTICE index 3ca981853..30268871a 100644 --- a/NOTICE +++ b/NOTICE @@ -3,3 +3,9 @@ Some parts of the framework, which are derived from, or inspired by https://gith PyQubo formulation of the TSP in function _create_pyqubo_model in file applications/TSP/mappings/ISING.py was kindly provided by AWS (Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved.) djs1000.tsp is copied from http://comopt.ifi.uni-heidelberg.de/software/TSPLIB95/ + +The implementation of /applications/qml/qleet is based on the open-source qLEET package https://github.com/QLemma/qleet. + +This software is meant for research and benchmarking purposes only. The authors do not take responsibility for any other use. + +This project was supported by the Bavarian research program (BayVFP) in the context of the BenchQC project. diff --git a/README.md b/README.md index 71a1f367e..200d76325 100644 --- a/README.md +++ b/README.md @@ -1,14 +1,18 @@ # QUARK: A Framework for Quantum Computing Application Benchmarking Quantum Computing Application Benchmark (QUARK) is a framework for orchestrating benchmarks of different industry applications on quantum computers. -QUARK supports various applications such as the traveling salesperson problem (TSP), the maximum satisfiability (MaxSAT) problem, robot path optimization in the PVC sealing use case +QUARK supports various applications such as the traveling salesperson problem (TSP), the maximum satisfiability (MaxSAT) problem, robot path optimization in the PVC sealing use case (PVC) as well as new additions like the Maximum Independent Set (MIS), Set Cover Problem (SCP) and Auto Carrier Loading (ACL). -It also features different solvers (e.g., simulated /quantum annealing and the quantum approximate optimization algorithm (QAOA)), quantum devices (e.g., IonQ and Rigetti), and simulators. + +It also includes two machine learning modules, namely generative modeling and image classification, which can be benchmarked with various training methods like quantum circuit born machine (QCBM) and quantum generative adversarial networks (QGANs) or a hybrid training method, respectively. +Several learning data sets were added for convenience. + +QUARK features different solvers (e.g., simulated /quantum annealing and the quantum approximate optimization algorithm (QAOA)), quantum devices (e.g., IonQ and Rigetti), and simulators. It is designed to be easily extendable in all of its components: applications, mappings, solvers, devices, and any other custom modules. ## Publications Details about the motivations for the original framework can be found in the [accompanying QUARK paper from Finžgar et al](https://arxiv.org/abs/2202.03028). -Even though the architecture changes significantly from QUARK 1.0 to the current version, the guiding principles still remain. The most recent publication from [Kiwit et al.](https://arxiv.org/abs/2308.04082) provides an updated overview of the functionalities and quantum machine learning features of QUARK. +Even though the architecture changes significantly from QUARK 1.0 to the current version, the guiding principles still remain. More recent publications from Kiwit et al. [[1](https://arxiv.org/abs/2308.04082), [2](https://link.springer.com/article/10.1007/s13218-024-00864-7)] provide an updated overview of the functionalities and quantum machine learning features of QUARK. ## Documentation Documentation with a tutorial and developer guidelines can be found here: https://quark-framework.readthedocs.io/en/dev/. @@ -54,6 +58,19 @@ python src/main.py > __Note:__ It is still possible to only use a subset of QUARK's modules without also using uv. The process is explained in the [documentation](https://quark-framework.readthedocs.io/en/dev/tutorial.html). +### Additional installation steps for macOS +On Mac machines with Apple Silicon, JAX must be installed separately. Do this after having installed all the other dependencies with the requirements file generated above. Following the [Apple developer guide](https://developer.apple.com/metal/jax/), you need to install the binaries, e.g., using +``` +python3 -m venv ~/jax-metal +source ~/jax-metal/bin/activate +python -m pip install numpy wheel +python -m pip install jax-metal +pip install -U jaxlib jax +```` +You also need to set an environment variable to run jax-metal with jaxlibs beyond the minimal version +```ENABLE_PJRT_COMPATIBILITY=1```. + + ## Git Large File Storage (LFS) QUARK stores data and config files using **Git LFS**. If you are contributing to this project or cloning this repository, ensure that you have Git LFS installed and configured to manage large files effectively. @@ -135,8 +152,9 @@ Example run (you need to check at least one option with an ``X`` for the checkbo MIS SCP GenerativeModeling + Classification -2024-10-09 15:05:52,610 [INFO] Import module modules.applications.optimization.TSP.TSP +2025-05-05 15:05:52,610 [INFO] Import module modules.applications.optimization.tsp.tsp [?] (Option for TSP) How many nodes does you graph need?: > [X] 3 [ ] 4 @@ -154,38 +172,38 @@ Example run (you need to check at least one option with an ``X`` for the checkbo [ ] ReverseGreedyClassicalTSP [ ] RandomTSP -2024-10-09 15:06:20,897 [INFO] Import module modules.solvers.GreedyClassicalTSP -2024-10-09 15:06:20,933 [INFO] Skipping asking for submodule, since only 1 option (Local) is available. -2024-10-09 15:06:20,933 [INFO] Import module modules.devices.Local -2024-10-09 15:06:20,946 [INFO] Submodule configuration finished +2025-05-05 15:06:20,897 [INFO] Import module modules.solvers.greedy_classical_tsp +2025-05-05 15:06:20,933 [INFO] Skipping asking for submodule, since only 1 option (Local) is available. +2025-05-05 15:06:20,933 [INFO] Import module modules.devices.Local +2025-05-05 15:06:20,946 [INFO] Submodule configuration finished [?] How many repetitions do you want?: 1P -2024-10-09 15:07:11,573 [INFO] Import module modules.applications.optimization.TSP.TSP -2024-10-09 15:07:11,573 [INFO] Import module modules.solvers.GreedyClassicalTSP -2024-10-09 15:07:11,574 [INFO] Import module modules.devices.Local -2024-10-09 15:07:12,194 [INFO] [INFO] Created Benchmark run directory /Users/user1/quark/benchmark_runs/tsp-2024-10-09-15-07-11 -2024-10-09 15:07:12,194 [INFO] Codebase is based on revision 1d9d17aad7ddff623ff51f62ca3ec2756621c345 and has no uncommitted changes -2024-10-09 15:07:12,195 [INFO] Running backlog item 1/1, Iteration 1/1: -2024-10-09 15:07:12,386 [INFO] Route found: +2025-05-05 15:07:11,573 [INFO] Import module modules.applications.optimization.tsp.tsp +2025-05-05 15:07:11,573 [INFO] Import module modules.solvers.greedy_classical_tsp +2025-05-05 15:07:11,574 [INFO] Import module modules.devices.Local +2025-05-05 15:07:12,194 [INFO] [INFO] Created Benchmark run directory /Users/user1/quark/benchmark_runs/tsp-2025-05-05-15-07-11 +2025-05-05 15:07:12,194 [INFO] Codebase is based on revision 1d9d17aad7ddff623ff51f62ca3ec2756621c345 and has no uncommitted changes +2025-05-05 15:07:12,195 [INFO] Running backlog item 1/1, Iteration 1/1: +2025-05-05 15:07:12,386 [INFO] Route found: Node 0 -> Node 2 -> Node 1 -2024-10-09 15:07:12,386 [INFO] All 3 nodes got visited -2024-10-09 15:07:12,386 [INFO] Total distance (without return): 727223.0 -2024-10-09 15:07:12,386 [INFO] Total distance (including return): 1436368.0 -2024-10-09 15:07:12,386 [INFO] -2024-10-09 15:07:12,386 [INFO] ==== Run backlog item 1/1 with 1 iterations - FINISHED:1 ==== -2024-10-09 15:07:12,387 [INFO] -2024-10-09 15:07:12,387 [INFO] =============== Run finished =============== -2024-10-09 15:07:12,387 [INFO] -2024-10-09 15:07:12,387 [INFO] ================================================================================ -2024-10-09 15:07:12,387 [INFO] ====== Run 1 backlog items with 1 iterations - FINISHED:1 -2024-10-09 15:07:12,387 [INFO] ================================================================================ -2024-10-09 15:07:12,395 [INFO] -2024-10-09 15:07:12,400 [INFO] Saving 1 benchmark records to /Users/user1/QUARK/benchmark_runs/tsp-2024-10-09-15-07-11/results.json -2024-10-09 15:07:12,942 [INFO] Finished creating plots. -2024-10-09 15:07:12,943 [INFO] ============================================================ -2024-10-09 15:07:12,944 [INFO] ==================== QUARK finished! ==================== -2024-10-09 15:07:12,944 [INFO] ============================================================ +2025-05-05 15:07:12,386 [INFO] All 3 nodes got visited +2025-05-05 15:07:12,386 [INFO] Total distance (without return): 727223.0 +2025-05-05 15:07:12,386 [INFO] Total distance (including return): 1436368.0 +2025-05-05 15:07:12,386 [INFO] +2025-05-05 15:07:12,386 [INFO] ==== Run backlog item 1/1 with 1 iterations - FINISHED:1 ==== +2025-05-05 15:07:12,387 [INFO] +2025-05-05 15:07:12,387 [INFO] =============== Run finished =============== +2025-05-05 15:07:12,387 [INFO] +2025-05-05 15:07:12,387 [INFO] ================================================================================ +2025-05-05 15:07:12,387 [INFO] ====== Run 1 backlog items with 1 iterations - FINISHED:1 +2025-05-05 15:07:12,387 [INFO] ================================================================================ +2025-05-05 15:07:12,395 [INFO] +2025-05-05 15:07:12,400 [INFO] Saving 1 benchmark records to /Users/user1/QUARK/benchmark_runs/tsp-2025-05-05-15-07-11/results.json +2025-05-05 15:07:12,942 [INFO] Finished creating plots. +2025-05-05 15:07:12,943 [INFO] ============================================================ +2025-05-05 15:07:12,944 [INFO] ==================== QUARK finished! ==================== +2025-05-05 15:07:12,944 [INFO] ============================================================ ``` @@ -194,11 +212,11 @@ All used config files, logs and results are stored in a folder in the ```benchma ### Interrupt/resume The processing of backlog items may get interrupted in which case you will see something like ``` -2024-03-13 10:25:20,201 [INFO] ================================================================================ -2024-03-13 10:25:20,201 [INFO] ====== Run 3 backlog items with 10 iterations - FINISHED:15 INTERRUPTED:15 -2024-03-13 10:25:20,201 [INFO] ====== There are interrupted jobs. You may resume them by running QUARK with -2024-03-13 10:25:20,201 [INFO] ====== --resume-dir=benchmark_runs\tsp-2024-03-13-10-25-19 -2024-03-13 10:25:20,201 [INFO] ================================================================================ +2025-05-05 15:09:21,001 [INFO] ================================================================================ +2025-05-05 15:09:21,001 [INFO] ====== Run 3 backlog items with 10 iterations - FINISHED:15 INTERRUPTED:15 +2025-05-05 15:09:21,001 [INFO] ====== There are interrupted jobs. You may resume them by running QUARK with +2025-05-05 15:09:21,001 [INFO] ====== --resume-dir=benchmark_runs\tsp-2025-05-05-15-09-20 +2025-05-05 15:09:21,001 [INFO] ================================================================================ ``` This happens if you press CTRL-C or if some QUARK module does its work asynchronously, e.g. by submitting its job to some batch system. Learn more about how to write asynchronous modules in the [developer guide](https://quark-framework.readthedocs.io/en/dev/developer.html). @@ -215,7 +233,7 @@ After making sure your docker daemon is running, you can run the container: docker run -it --rm ghcr.io/quark-framework/quark ``` -> __Note__: ARM builds are (temporarily) removed in release 2.1.3 because pyqubo 1.5.0 is unavailable for this platform +> __Note__: ARM builds are (temporarily) removed in release 2.1.5 because pyscipopt 5.0 is unavailable for this platform > at the moment. This means if you want to run QUARK as a container on a machine with a chip from this > [list](https://en.wikipedia.org/wiki/List_of_ARM_processors) you might face problems. Please feel free to > [open an issue](https://github.com/QUARK-framework/QUARK/issues/new), so we can work on a tailored workaround until diff --git a/docs/conf.py b/docs/conf.py index c47101937..034322e27 100644 --- a/docs/conf.py +++ b/docs/conf.py @@ -45,8 +45,8 @@ autosummary_generate = True # Turn on sphinx.ext.autosummary autosummary_mock_imports = [ - 'modules.applications.optimization.PVC.createReferenceGraph', - 'modules.applications.optimization.TSP.createReferenceGraph' + 'modules.applications.optimization.pvc.createReferenceGraph', + 'modules.applications.optimization.tsp.createReferenceGraph' ] # If true, the current module name will be prepended to all description # unit titles (such as .. function::). diff --git a/docs/tutorial.rst b/docs/tutorial.rst index 5b6bdfda2..557a1eb76 100644 --- a/docs/tutorial.rst +++ b/docs/tutorial.rst @@ -182,7 +182,7 @@ Example run (You need to check at least one option with an ``X`` for the checkbo SCP GenerativeModeling - 2024-10-09 15:05:52,610 [INFO] Import module modules.applications.optimization.TSP.TSP + 2024-10-09 15:05:52,610 [INFO] Import module modules.applications.optimization.tsp.tsp [?] (Option for TSP) How many nodes does you graph need?: > [X] 3 [ ] 4 @@ -200,13 +200,13 @@ Example run (You need to check at least one option with an ``X`` for the checkbo [ ] ReverseGreedyClassicalTSP [ ] RandomTSP - 2024-10-09 15:06:20,897 [INFO] Import module modules.solvers.GreedyClassicalTSP + 2024-10-09 15:06:20,897 [INFO] Import module modules.solvers.greedy_classical_tsp 2024-10-09 15:06:20,933 [INFO] Skipping asking for submodule, since only 1 option (Local) is available. 2024-10-09 15:06:20,933 [INFO] Import module modules.devices.Local 2024-10-09 15:06:20,946 [INFO] Submodule configuration finished [?] How many repetitions do you want?: 1P - 2024-10-09 15:07:11,573 [INFO] Import module modules.applications.optimization.TSP.TSP - 2024-10-09 15:07:11,573 [INFO] Import module modules.solvers.GreedyClassicalTSP + 2024-10-09 15:07:11,573 [INFO] Import module modules.applications.optimization.tsp.tsp + 2024-10-09 15:07:11,573 [INFO] Import module modules.solvers.greedy_classical_tsp 2024-10-09 15:07:11,574 [INFO] Import module modules.devices.Local 2024-10-09 15:07:12,194 [INFO] [INFO] Created Benchmark run directory /Users/user1/quark/benchmark_runs/tsp-2024-10-09-15-07-11 2024-10-09 15:07:12,194 [INFO] Codebase is based on revision 1d9d17aad7ddff623ff51f62ca3ec2756621c345 and has no uncommitted changes @@ -267,7 +267,7 @@ After making sure your docker daemon is running, you can run the container: docker run -it --rm ghcr.io/quark-framework/quark -**Note**: ARM builds are (temporarily) removed in release 2.1.3 because pyqubo 1.5.0 is unavailable for this platform at +**Note**: ARM builds are (temporarily) removed in release 2.1.5 because pyscipopt 5.0 is unavailable for this platform at the moment. This means if you want to run QUARK as a container on a machine with a chip from this `list `_ you might face problems. Please feel free to `open an issue `_, so we can work on a tailored workaround until the latest @@ -359,12 +359,12 @@ An example for this would be: [ { "name": "TSP", - "module": "modules.applications.optimization.TSP.TSP", + "module": "modules.applications.optimization.tsp.tsp", "dir": "src", "submodules": [ { "name": "GreedyClassicalTSP", - "module": "modules.solvers.GreedyClassicalTSP", + "module": "modules.solvers.greedy_classical_tsp", "submodules": [] } ] diff --git a/src/BenchmarkManager.py b/src/benchmark_manager.py similarity index 99% rename from src/BenchmarkManager.py rename to src/benchmark_manager.py index 88e050a72..f76f42732 100644 --- a/src/BenchmarkManager.py +++ b/src/benchmark_manager.py @@ -25,10 +25,10 @@ import numpy as np -from BenchmarkRecord import BenchmarkRecord, BenchmarkRecordStored -from ConfigManager import ConfigManager -from modules.Core import Core -from Plotter import Plotter +from config_manager import ConfigManager +from benchmark_record import BenchmarkRecord, BenchmarkRecordStored +from plotter import Plotter +from modules.core import Core from utils import get_git_revision from utils_mpi import get_comm diff --git a/src/BenchmarkRecord.py b/src/benchmark_record.py similarity index 99% rename from src/BenchmarkRecord.py rename to src/benchmark_record.py index d720eef57..84686c94b 100644 --- a/src/BenchmarkRecord.py +++ b/src/benchmark_record.py @@ -17,7 +17,7 @@ from copy import deepcopy from typing import final -from Metrics import Metrics +from metrics import Metrics class BenchmarkRecord: diff --git a/src/ConfigManager.py b/src/config_manager.py similarity index 98% rename from src/ConfigManager.py rename to src/config_manager.py index 90e3d8ee1..c8455f174 100644 --- a/src/ConfigManager.py +++ b/src/config_manager.py @@ -12,23 +12,24 @@ # See the License for the specific language governing permissions and # limitations under the License. +from utils import _get_instance_with_sub_options, checkbox +from modules.applications.application import Application +from modules.core import Core +from typing_extensions import TypedDict, NotRequired, Self +import yaml +import numpy as np +import networkx as nx +from matplotlib import pyplot as plt import itertools import logging import re import inquirer -import matplotlib.pyplot as plt -import networkx as nx -import numpy as np -import yaml -from typing_extensions import NotRequired, Self, TypedDict - -from modules.applications import Application -from modules.Core import Core -from utils import _get_instance_with_sub_options, checkbox +import matplotlib +matplotlib.use("Agg") # Use a non-interactive backend for matplotlib -class ConfigModule(TypedDict): # pylint: disable=R0903 +class ConfigModule(TypedDict): """ Each instance consists of the name of the module, its config, and a list of its configured submodules, which are ConfigModule instances themselves. It can also contain the instance of the class associated to this module, @@ -40,7 +41,7 @@ class ConfigModule(TypedDict): # pylint: disable=R0903 instance: NotRequired[Core] -class BenchmarkConfig(TypedDict): # pylint: disable=R0903 +class BenchmarkConfig(TypedDict): """ Each instance consists of a ConfigModule associated with the application at the first level and the number of repetitions of the benchmark. diff --git a/src/demo/instruction_demo.py b/src/demo/instruction_demo.py index 334b64ab9..857dbc434 100644 --- a/src/demo/instruction_demo.py +++ b/src/demo/instruction_demo.py @@ -1,8 +1,8 @@ import logging -from BenchmarkManager import Instruction -from modules.applications.Application import Application -from modules.Core import Core +from benchmark_manager import Instruction +from modules.core import Core +from modules.applications.application import Application class InstructionDemo(Application): diff --git a/src/Installer.py b/src/installer.py similarity index 91% rename from src/Installer.py rename to src/installer.py index 091b8518b..fdedf229f 100644 --- a/src/Installer.py +++ b/src/installer.py @@ -12,19 +12,19 @@ # See the License for the specific language governing permissions and # limitations under the License. -import inspect -import json import logging +import json import os import time from pathlib import Path +import inspect -import inquirer import yaml from packaging import version +import inquirer -from modules.Core import Core -from utils import _get_instance_with_sub_options, checkbox, get_git_revision +from modules.core import Core +from utils import _get_instance_with_sub_options, get_git_revision, checkbox class Installer: @@ -40,14 +40,19 @@ def __init__(self): self.python_version = "3.12.7" self.pip_version = "23.0" self.default_app_modules = [ - {"name": "PVC", "class": "PVC", "module": "modules.applications.optimization.PVC.PVC"}, - {"name": "SAT", "class": "SAT", "module": "modules.applications.optimization.SAT.SAT"}, - {"name": "TSP", "class": "TSP", "module": "modules.applications.optimization.TSP.TSP"}, - {"name": "ACL", "class": "ACL", "module": "modules.applications.optimization.ACL.ACL"}, - {"name": "MIS", "class": "MIS", "module": "modules.applications.optimization.MIS.MIS"}, - {"name": "SCP", "class": "SCP", "module": "modules.applications.optimization.SCP.SCP"}, + {"name": "PVC", "class": "PVC", "module": "modules.applications.optimization.pvc.pvc"}, + {"name": "SAT", "class": "SAT", "module": "modules.applications.optimization.sat.sat"}, + {"name": "TSP", "class": "TSP", "module": "modules.applications.optimization.tsp.tsp"}, + {"name": "ACL", "class": "ACL", "module": "modules.applications.optimization.acl.acl"}, + {"name": "MIS", "class": "MIS", "module": "modules.applications.optimization.mis.mis"}, + {"name": "SCP", "class": "SCP", "module": "modules.applications.optimization.scp.scp"}, + {"name": "BP", "class": "BP", "module": "modules.applications.optimization.bp.bp"}, + {"name": "SALBP", "class": "SALBP", "module": "modules.applications.optimization.salbp.salbp"}, {"name": "GenerativeModeling", "class": "GenerativeModeling", - "module": "modules.applications.qml.generative_modeling.GenerativeModeling"} + "module": "modules.applications.qml.generative_modeling.generative_modeling"}, + {"name": "Classification", "class": "Classification", + "module": "modules.applications.qml.classification.classification"} + ] self.core_requirements = [ @@ -96,26 +101,26 @@ def configure(self, env_name="default") -> None: self.save_env(module_db, env_name) requirements = self.collect_requirements(module_db["modules"]) - # activate_requirements = checkbox("requirements", "Should we create an package file, if yes for " - # "which package manager?", - # ["Conda", "PIP", "Print it here"])["requirements"] - - # if "Conda" in activate_requirements: - # self.create_conda_file(requirements, env_name) - # if "PIP" in activate_requirements: - self.create_req_file(requirements, env_name) - # if "Print it here" in activate_requirements: - # logging.info("Please install:") - # for p, v in requirements.items(): - # logging.info(f" - {p}{': ' + v[0] if v else ''}") - - # activate_answer = inquirer.prompt([ - # inquirer.List("activate", - # message="Do you want to activate the QUARK module environment?", - # choices=["Yes", "No"])])["activate"] - - # if activate_answer == "Yes": - self.set_active_env(env_name) + activate_requirements = checkbox("requirements", "Should we create an package file, if yes for " + "which package manager?", + ["Conda", "PIP", "Print it here"])["requirements"] + + if "Conda" in activate_requirements: + self.create_conda_file(requirements, env_name) + if "PIP" in activate_requirements: + self.create_req_file(requirements, env_name) + if "Print it here" in activate_requirements: + logging.info("Please install:") + for p, v in requirements.items(): + logging.info(f" - {p}{': ' + v[0] if v else ''}") + + activate_answer = inquirer.prompt([ + inquirer.List("activate", + message="Do you want to activate the QUARK module environment?", + choices=["Yes", "No"])])["activate"] + + if activate_answer == "Yes": + self.set_active_env(env_name) def check_for_configs(self) -> list: """ diff --git a/src/main.py b/src/main.py index 680f9df9c..44833a0c8 100644 --- a/src/main.py +++ b/src/main.py @@ -21,7 +21,7 @@ import yaml -from Installer import Installer +from installer import Installer from utils import _expand_paths from utils_mpi import MPIFileHandler, MPIStreamHandler, get_comm @@ -55,6 +55,7 @@ def setup_logging() -> None: Sets up the logging. """ logging.root.handlers = [] + logging.getLogger("qiskit").setLevel(logging.WARNING) logging.basicConfig( level=logging.INFO, format="%(asctime)s [%(levelname)s] %(message)s", @@ -97,8 +98,8 @@ def start_benchmark_run(config_file: str = None, store_dir: str = None, benchmark_config = json.loads(benchmark_config["config"]) - from BenchmarkManager import BenchmarkManager # pylint: disable=C0415 - from ConfigManager import ConfigManager # pylint: disable=C0415 + from benchmark_manager import BenchmarkManager # pylint: disable=C0415 + from config_manager import ConfigManager # pylint: disable=C0415 config_manager = ConfigManager() config_manager.set_config(benchmark_config) @@ -147,15 +148,15 @@ def handle_benchmark_run(args: argparse.Namespace) -> None: :param args: Namespace with the arguments given by the user """ - from BenchmarkManager import BenchmarkManager # pylint: disable=C0415 - from Plotter import Plotter # pylint: disable=C0415 + from benchmark_manager import BenchmarkManager # pylint: disable=C0415 + from plotter import Plotter # pylint: disable=C0415 benchmark_manager = BenchmarkManager(fail_fast=args.failfast) if args.summarize: benchmark_manager.summarize_results(args.summarize) else: - from ConfigManager import ConfigManager # pylint: disable=C0415 + from config_manager import ConfigManager # pylint: disable=C0415 config_manager = ConfigManager() if args.modules: logging.info(f"Load application modules configuration from {args.modules}") diff --git a/src/Metrics.py b/src/metrics.py similarity index 100% rename from src/Metrics.py rename to src/metrics.py diff --git a/src/modules/applications/Application.py b/src/modules/applications/application.py similarity index 97% rename from src/modules/applications/Application.py rename to src/modules/applications/application.py index cdfa37d27..35b3f1365 100644 --- a/src/modules/applications/Application.py +++ b/src/modules/applications/application.py @@ -13,8 +13,7 @@ # limitations under the License. from abc import ABC, abstractmethod - -from modules.Core import Core +from modules.core import Core class Application(Core, ABC): diff --git a/src/modules/applications/Mapping.py b/src/modules/applications/mapping.py similarity index 98% rename from src/modules/applications/Mapping.py rename to src/modules/applications/mapping.py index f0a918673..67ab53666 100644 --- a/src/modules/applications/Mapping.py +++ b/src/modules/applications/mapping.py @@ -13,8 +13,7 @@ # limitations under the License. from abc import ABC, abstractmethod - -from modules.Core import Core +from modules.core import Core class Mapping(Core, ABC): diff --git a/src/modules/applications/optimization/ACL/Vehicle_data_QUARK.xlsx b/src/modules/applications/optimization/acl/Vehicle_data_QUARK.xlsx similarity index 100% rename from src/modules/applications/optimization/ACL/Vehicle_data_QUARK.xlsx rename to src/modules/applications/optimization/acl/Vehicle_data_QUARK.xlsx diff --git a/src/modules/applications/optimization/ACL/__init__.py b/src/modules/applications/optimization/acl/__init__.py similarity index 100% rename from src/modules/applications/optimization/ACL/__init__.py rename to src/modules/applications/optimization/acl/__init__.py diff --git a/src/modules/applications/optimization/ACL/ACL.py b/src/modules/applications/optimization/acl/acl.py similarity index 98% rename from src/modules/applications/optimization/ACL/ACL.py rename to src/modules/applications/optimization/acl/acl.py index 43b1fa28a..1581e932d 100644 --- a/src/modules/applications/optimization/ACL/ACL.py +++ b/src/modules/applications/optimization/acl/acl.py @@ -26,17 +26,17 @@ # The above copyright notice and this permission notice shall be included # in all copies or substantial portions of the Software. -import logging import os +import logging from typing import TypedDict -import numpy as np import pandas as pd +import numpy as np import pulp -from modules.applications.Application import Core -from modules.applications.optimization.Optimization import Optimization -from utils import end_time_measurement, start_time_measurement +from modules.applications.application import Core +from modules.applications.optimization.optimization import Optimization +from utils import start_time_measurement, end_time_measurement class ACL(Optimization): @@ -84,12 +84,10 @@ def get_default_submodule(self, option: str) -> Core: :raises NotImplementedError: If the option is not recognized """ if option == "MIPsolverACL": - from modules.solvers.MIPsolverACL import \ - MIPaclp # pylint: disable=C0415 + from modules.solvers.mip_solver_acl import MIPaclp # pylint: disable=C0415 return MIPaclp() elif option == "QUBO": - from modules.applications.optimization.ACL.mappings.QUBO import \ - Qubo # pylint: disable=C0415 + from modules.applications.optimization.acl.mappings.qubo import Qubo # pylint: disable=C0415 return Qubo() else: raise NotImplementedError(f"Submodule Option {option} not implemented") diff --git a/src/modules/applications/optimization/ACL/mappings/__init__.py b/src/modules/applications/optimization/acl/mappings/__init__.py similarity index 100% rename from src/modules/applications/optimization/ACL/mappings/__init__.py rename to src/modules/applications/optimization/acl/mappings/__init__.py diff --git a/src/modules/applications/optimization/ACL/mappings/ISING.py b/src/modules/applications/optimization/acl/mappings/ising.py similarity index 96% rename from src/modules/applications/optimization/ACL/mappings/ISING.py rename to src/modules/applications/optimization/acl/mappings/ising.py index 1abb7a922..9995707ea 100644 --- a/src/modules/applications/optimization/ACL/mappings/ISING.py +++ b/src/modules/applications/optimization/acl/mappings/ising.py @@ -20,9 +20,9 @@ from qiskit_optimization import QuadraticProgram from qiskit_optimization.converters import QuadraticProgramToQubo -from modules.applications.Mapping import Mapping -from modules.Core import Core -from utils import end_time_measurement, start_time_measurement +from modules.applications.mapping import Mapping +from modules.core import Core +from utils import start_time_measurement, end_time_measurement class Ising(Mapping): @@ -200,10 +200,10 @@ def get_default_submodule(self, option: str) -> Core: :raises NotImplementedError: If the option is not recognized """ if option == "QAOA": - from modules.solvers.QAOA import QAOA # pylint: disable=C0415 + from modules.solvers.qaoa import QAOA # pylint: disable=C0415 return QAOA() elif option == "QiskitQAOA": - from modules.solvers.QiskitQAOA import QiskitQAOA # pylint: disable=C0415 + from modules.solvers.qiskit_qaoa import QiskitQAOA # pylint: disable=C0415 return QiskitQAOA() else: raise NotImplementedError(f"Solver Option {option} not implemented") diff --git a/src/modules/applications/optimization/ACL/mappings/QUBO.py b/src/modules/applications/optimization/acl/mappings/qubo.py similarity index 96% rename from src/modules/applications/optimization/ACL/mappings/QUBO.py rename to src/modules/applications/optimization/acl/mappings/qubo.py index 75661fc96..632c1f4bd 100644 --- a/src/modules/applications/optimization/ACL/mappings/QUBO.py +++ b/src/modules/applications/optimization/acl/mappings/qubo.py @@ -12,18 +12,19 @@ # See the License for the specific language governing permissions and # limitations under the License. -import logging -import re from typing import TypedDict +import re +import logging import numpy as np from qiskit_optimization import QuadraticProgram -from qiskit_optimization.converters import (InequalityToEquality, - IntegerToBinary, - LinearEqualityToPenalty) +from qiskit_optimization.converters import ( + InequalityToEquality, IntegerToBinary, + LinearEqualityToPenalty +) -from modules.applications.Mapping import Core, Mapping -from utils import end_time_measurement, start_time_measurement +from modules.applications.mapping import Mapping, Core +from utils import start_time_measurement, end_time_measurement # TODO Large chunks of this code is duplicated in ACL.mappings.ISING -> unify @@ -268,7 +269,7 @@ def get_default_submodule(self, option: str) -> Core: :raises NotImplementedError: If the option is not recognized """ if option == "Annealer": - from modules.solvers.Annealer import Annealer # pylint: disable=C0415 + from modules.solvers.annealer import Annealer # pylint: disable=C0415 return Annealer() else: raise NotImplementedError(f"Solver Option {option} not implemented") diff --git a/src/modules/applications/optimization/bp/__init__.py b/src/modules/applications/optimization/bp/__init__.py new file mode 100644 index 000000000..da183153b --- /dev/null +++ b/src/modules/applications/optimization/bp/__init__.py @@ -0,0 +1,15 @@ +# Copyright 2022 The QUARK Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +"""Module for bp""" diff --git a/src/modules/applications/optimization/bp/bp.py b/src/modules/applications/optimization/bp/bp.py new file mode 100644 index 000000000..8b9491180 --- /dev/null +++ b/src/modules/applications/optimization/bp/bp.py @@ -0,0 +1,318 @@ +# Copyright 2021 The QUARK Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from typing import TypedDict +import random +import math +import logging + +from docplex.mp.model import Model +from qiskit_optimization import QuadraticProgram +from qiskit_optimization.translators import from_docplex_mp +from qiskit_optimization.converters import InequalityToEquality, IntegerToBinary, LinearEqualityToPenalty + +from modules.applications.application import Application +from modules.core import Core +from modules.applications.optimization.bp.mappings.mip import MIP +from modules.applications.optimization.optimization import Optimization +from utils import start_time_measurement, end_time_measurement + + +class BP(Optimization): + """ + The bin packing problem is a classic optimization challenge where items of varying sizes must be efficiently packed + into a finite number of bins, each with a fixed capacity, aiming to minimize the number of bins utilized. This + problem is computationally NP-hard, meaning that finding an exact solution in a reasonable time frame is often + impractical for large datasets. Consequently, various approximation algorithms have been developed to provide + near-optimal solutions within acceptable time limits. + + In practical applications, bin packing is prevalent in industries such as logistics and manufacturing. For instance, + it is used in loading trucks with weight capacity constraints, filling containers to maximize space utilization, and + creating file backups in media storage. Additionally, it plays a role in technology mapping for FPGA semiconductor + chip design, where efficient resource allocation is crucial. + + To address the bin packing problem, several heuristic and approximation methods have been proposed. One common + approach is the First-Fit Decreasing (FFD) algorithm, which involves sorting items in descending order by size and + then placing each item into the first bin that can accommodate it. While FFD does not always yield an optimal + solution, it is effective and widely used due to its simplicity and efficiency. Other advanced techniques, such as + Best-Fit and Karmarkar–Karp algorithms, offer improved performance for specific scenarios by considering different + strategies for item placement and bin selection. + (source: https://en.wikipedia.org/wiki/Bin_packing_problem) + """ + + def __init__(self): + """ + Constructor method + """ + super().__init__("BinPacking") + self.submodule_options = ["MIP", "Ising", "QUBO"] + + @staticmethod + def get_requirements() -> list: + """ + Return requirements of this module. + + :return: List of dict with requirements of this module + """ + return [ + {"name": "numpy", "version": "1.26.4"}, + {"name": "qiskit_optimization", "version": "0.6.1"}, + {"name": "docplex", "version": "2.25.236"} + ] + + def get_solution_quality_unit(self) -> str: + """ + Returns the unit of measurement for the solution quality. + + :return: Unit of measurement for the solution quality + """ + return "number_of_bins" + + def get_default_submodule(self, option: str) -> Core: + """ + Returns the default submodule based on the provided option. + + :param option: Option specifying the submodule + :return: Instance of the corresponding submodule + :raises NotImplementedError: If the option is not recognized + """ + if option == "Ising": + from modules.applications.optimization.bp.mappings.ising import Ising # pylint: disable=C0415 + return Ising() + elif option == "QUBO": + from modules.applications.optimization.bp.mappings.qubo import QUBO # pylint: disable=C0415 + return QUBO() + elif option == "MIP": + from modules.applications.optimization.bp.mappings.mip import MIP # pylint: disable=C0415 + return MIP() + else: + raise NotImplementedError(f"Mapping Option {option} not implemented") + + def get_parameter_options(self) -> dict: + """ + Returns the configurable settings for this application. + + :return: + .. code-block:: python + + return { + "number_of_objects": { + "values": list([3,4,5,6,7,8,9,10,15,20]), + "description": "How many objects do you want to fit inside the bins?", + }, + "instance_creating_mode": { + "values": list(["linear weights without incompatibilities", + "linear weights with incompatibilities", + "random weights without incompatibilities", + "random weights with incompatibilities"]), + "description": "How do you want to create the object weights?" + } + } + """ + return { + "number_of_objects": { + "values": [3, 4, 5, 6, 7, 8, 9, 10, 15, 20], + "description": "How many objects do you want to fit inside the bins?", + }, + "instance_creating_mode": { + "values": [ + "linear weights without incompatibilities", + "linear weights with incompatibilities", + "random weights without incompatibilities", + "random weights with incompatibilities" + ], + "description": "How do you want to create the object weights?" + } + } + + class Config(TypedDict): + """ + Attributes of a valid config. + + .. code-block:: python + + number_of_objects: int + instance_creating_mode: str + + """ + number_of_objects: int + instance_creating_mode: str + + def create_bin_packing_instance(self, number_of_objects: int, mode: str) -> tuple[list, int, list]: + """ + Generates a bin packing problem instance depending on the mode and the number of objects. + + :param number_of_objects: How many objects should the bin packing problem instance consist of + :param mode: Declares the mode with which the bin packing problem instance should be created + :return: Tuple with object_weights, bin_capacity, incompatible_objects + """ + if mode == "linear weights without incompatibilities": + object_weights = list(range(1, number_of_objects + 1)) + bin_capacity = max(object_weights) + incompatible_objects = [] + + elif mode == "linear weights with incompatibilities": + object_weights = list(range(1, number_of_objects + 1)) + bin_capacity = max(object_weights) + incompatible_objects = [] + # add some incompatible objects via a for-loop + for i in range(math.floor(number_of_objects / 2)): + incompatible_objects.append((i, number_of_objects - 1 - i)) + + elif mode == "random weights without incompatibilities": + object_weights = [random.randint(1, number_of_objects) for _ in range(number_of_objects)] + bin_capacity = max(object_weights) + incompatible_objects = [] + + elif mode == "random weights with incompatibilities": + object_weights = [random.randint(1, number_of_objects) for _ in range(number_of_objects)] + bin_capacity = max(object_weights) + incompatible_objects = [] + for i in range(math.floor(number_of_objects / 2)): + incompatible_objects.append((i, number_of_objects - i)) + + else: + logging.error("An error occurred. Couldn't create a bin packing instance") + raise ValueError("forbidden mode during bin-packing-instance-creating-process") + + return object_weights, bin_capacity, incompatible_objects + + def generate_problem(self, config: Config) -> tuple[list, float, list]: + """ + Generates a bin-packing problem instance with the input configuration. + + :param config: Configuration dictionary with problem settings + :return: Tuple with object_weights, bin_capacity, incompatible_objects + """ + if config is None: + config = { + "number_of_objects": 5, + "instance_creating_mode": "linear weights without incompatibilities" + } + + number_of_objects = config['number_of_objects'] + instance_creating_mode = config['instance_creating_mode'] + + self.object_weights, self.bin_capacity, self.incompatible_objects = self.create_bin_packing_instance( + number_of_objects, instance_creating_mode + ) + + return self.object_weights, self.bin_capacity, self.incompatible_objects + + @staticmethod + def detect_mapping_from_solution(solution: dict) -> str: + """ + Detects the mapping type based on the solution format. + + :param solution: A dictionary representing the solution + :return: The detected mapping type + """ + if solution is None: + return "Invalid" + + # The solution always contains slack variables if it was mapped to a QUBO or ISING formulation. + if any('@int_slack@' in key for key in solution.keys()): + return "QUBO_like" + else: + return "MIP" + + def validate(self, solution: dict) -> tuple[bool, float]: + """ + Checks if a given solution is feasible for the problem instance. + + :param solution: List containing the nodes of the solution + :return: Boolean whether the solution is valid, time it took to validate + """ + start = start_time_measurement() + + if solution is None: + logging.warning("Solution is 'None'. Returning invalid solution status.") + return False, end_time_measurement(start) + + else: + # create the MIP to investigate the solution + problem_instance = (self.object_weights, self.bin_capacity, self.incompatible_objects) + self.mip_original = MIP.create_mip(self, problem_instance) + mapping = BP.detect_mapping_from_solution(solution) + + if mapping == "MIP": + # Transform docplex model to the qiskit-optimization framework + self.mip_qiskit = from_docplex_mp(self.mip_original) + # Put the solution-values into a list to be able to check feasibility + solution_list = [] + for key, value in solution.items(): + solution_list.append(value) + feasible_or_not = self.mip_qiskit.is_feasible(solution_list) + + elif mapping == "QUBO_like": # QUBO or Ising + + # Transform docplex model to the qiskit-optimization framework + self.mip_qiskit = from_docplex_mp(self.mip_original) + # Transform inequalities to equalities --> with slacks + mip_ineq2eq = InequalityToEquality().convert(self.mip_qiskit) + # Transform integer variables to binary variables -->split up into multiple binaries + self.mip_qiskit_int2bin = IntegerToBinary().convert(mip_ineq2eq) + + # Re-order the solution-values to be able to check feasibility -> because + # The variables are muddled in the dictionary + x_values = [] + y_values = [] + slack_values = [] + for key, value in solution.items(): + if key[0] == "x": # bin-variable + x_values.append(value) + elif key[0] == "y": # object-assignment-variable + y_values.append(value) + else: # slack-variable + slack_values.append(value) + solution_list = x_values + y_values + slack_values + feasible_or_not = self.mip_qiskit_int2bin.is_feasible(solution_list) + else: + logging.error("Error during validation.") + raise ValueError("Solution is 'None'.") + + return feasible_or_not, end_time_measurement(start) + + def evaluate(self, solution: dict) -> tuple[float, float]: + """ + Find the number of used bins for a given solution. + + :param solution: Dictionary containing the solution values + :return: Tour cost and the time it took to calculate it + """ + start = start_time_measurement() + + if solution is None: + return False, end_time_measurement(start) + else: + # Put the solution values into a list + mapping = BP.detect_mapping_from_solution(solution) + solution_list = [] + for keys, value in solution.items(): + solution_list.append(value) + + if mapping == "MIP": + obj_value = self.mip_qiskit.objective.evaluate(solution_list) + + elif mapping == "QUBO_like": # QUBO or Ising + obj_value = self.mip_qiskit_int2bin.objective.evaluate(solution_list) + + else: + logging.error('Error during validation. illegal mapping was used, please check') + obj_value = 'Please raise error' + + return obj_value, end_time_measurement(start) + + def save(self, path: str, iter_count: int) -> None: + pass diff --git a/src/modules/applications/optimization/bp/mappings/__init__.py b/src/modules/applications/optimization/bp/mappings/__init__.py new file mode 100644 index 000000000..e55f6cb83 --- /dev/null +++ b/src/modules/applications/optimization/bp/mappings/__init__.py @@ -0,0 +1,15 @@ +# Copyright 2022 The QUARK Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +"""Module for BP mappings""" diff --git a/src/modules/applications/optimization/bp/mappings/ising.py b/src/modules/applications/optimization/bp/mappings/ising.py new file mode 100644 index 000000000..c3e7a4ddf --- /dev/null +++ b/src/modules/applications/optimization/bp/mappings/ising.py @@ -0,0 +1,218 @@ +# Copyright 2021 The QUARK Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from typing import TypedDict +import numpy as np + +from modules.applications.mapping import Mapping +from modules.core import Core +from docplex.mp.model import Model + +from qiskit_optimization.translators import from_docplex_mp +from qiskit_optimization.converters import InequalityToEquality, IntegerToBinary +from qiskit_optimization import QuadraticProgram +from modules.applications.optimization.bp.mappings.qubo import QUBO +from modules.applications.optimization.bp.mappings.mip import MIP +from utils import start_time_measurement, end_time_measurement + + +class Ising(Mapping): + """ + Ising formulation for the Bin Packing Problem. + """ + + def __init__(self): + """ + Constructor method + """ + super().__init__() + self.submodule_options = ["QAOA", "PennylaneQAOA", "QiskitQAOA"] + self.key_mapping = None + self.graph = None + self.config = None + + @staticmethod + def get_requirements() -> list[dict]: + """ + Return requirements of this module + + :return: List of dict with requirements of this module + """ + return [{"name": "numpy", "version": "1.26.4"}, + {"name": "docplex", "version": "2.25.236"}] + + def get_parameter_options(self) -> dict: + """ + Returns the configurable settings for this mapping. + + :return: + .. code-block:: python + + return { + "penalty_factor": { + "values": [2], + "description": "Choose your QUBO-penalty-factor(s).", + "custom_input": True, + "allow_ranges": True, + "postproc": float + } + } + """ + return { + "penalty_factor": { + "values": [2], + "description": "Choose your QUBO-penalty-factor(s).", + "custom_input": True, + "allow_ranges": True, + "postproc": float # Since we allow custom input here we need to parse it to float (input is str) + } + } + + class Config(TypedDict): + """ + Attributes of a valid config. + + .. code-block:: python + + penalty_factor: float + + """ + penalty_factor: float + + def map(self, problem: tuple[list, float, list], config: Config) -> tuple[dict, float]: + """ + Maps the bin packing problem input to an ISING formulation. + + :param problem: Bin packing problem instance defined by + 1. object weights, 2. bin capacity, 3. incompatible objects + :param config: Config with the parameters specified in Config class + :return: Dict with ISING-matrix, -vector and -offset as well as time it took to map it + """ + self.problem = problem + self.config = config + start = start_time_measurement() + + # Create docplex model for the bin packing problem + bin_packing_mip = MIP.create_mip(self, problem) + + # Transform the MIP to an Ising formulation + penalty_factor = config['penalty_factor'] + self.ising_matrix, self.ising_vector, self.ising_offset, self.qubo = self.transform_docplex_mip_to_ising( + bin_packing_mip, penalty_factor + ) + + return { + "J": self.ising_matrix, + "t": self.ising_vector, + "c": self.ising_offset, + "QUBO": self.qubo + }, end_time_measurement(start) + + def reverse_map(self, solution: np.ndarray) -> tuple[dict, float]: + """ + Maps the solution back to be able to validate and evaluate it. + + :param solution: The solution of the QAOA is a numpy-array + :return: Solution mapped accordingly, time it took to map it + """ + start = start_time_measurement() + + solution_dict = {} + + variable_names = [var.name for var in self.qubo.variables] + + for idx in range(len(solution)): + var_name = variable_names[idx] + var_value = int(solution[len(solution) - 1 - idx]) # QAOA-result bitstring is reversed + solution_dict[var_name] = var_value + + return solution_dict, end_time_measurement(start) + + @staticmethod + def _convert_ising_to_qubo(solution: any) -> np.ndarray: + """ + Converts ISING format solution to QUBO. + + :param solution: Solution in ISING format + :return: Solution converted to QUBO format + """ + solution = np.array(solution) + with np.nditer(solution, op_flags=['readwrite']) as it: + for x in it: + if x == -1: + x[...] = 0 + return solution + + def transform_docplex_mip_to_ising(self, mip_docplex: Model, penalty_factor) -> ( + tuple)[np.ndarray, np.ndarray, float, QuadraticProgram]: + """ + Transform a docplex mix-integer-problem to an Ising formulation. + + :param mip_docplex: Docplex-Model + :param penalty_factor: Penalty factor for transformation + :return: J-matrix, t-vector and c-offset of the Ising formulation, and the QUBO matrix + """ + # Generate the QUBO with binary variables in {0; 1} + qubo_instance = QUBO() + _, qubo = qubo_instance.transform_docplex_mip_to_qubo(mip_docplex, penalty_factor) + + # Transform it to an Ising formulation + # --> x in {0; 1} gets transformed the following way via y in {-1; 1}: x = 1/2 * (1 - y) + # 0 --> 1 and 1 --> -1 + # in the following we construct a matrix J, a vector h and an offset c for the Ising formulation + # so that we have an equivalent formulation of the QUBO: obj = x J x^T + h x + c + num_qubits = len(qubo.variables) + ising_matrix = np.zeros((num_qubits, num_qubits), dtype=np.float64) + ising_vector = np.zeros(num_qubits, dtype=np.float64) + + ising_offset = qubo.objective.constant + + for idx, coeff in qubo.objective.linear.to_dict().items(): + ising_vector[idx] -= 1 / 2 * coeff + ising_offset += 1 / 2 * coeff + + for (i, j), coeff in qubo.objective.quadratic.to_dict().items(): + if i == j: + # Because the quadratic term x_i * x_j reduces to 1 if the x are ising + # variables in {-1, 1} --> another constant term + ising_offset += 1 / 2 * coeff + else: + ising_matrix[i, j] += 1 / 4 * coeff + ising_offset += 1 / 4 * coeff + + ising_vector[i] -= 1 / 4 * coeff + ising_vector[j] -= 1 / 4 * coeff + + return ising_matrix, ising_vector, ising_offset, qubo + + def get_default_submodule(self, option: str) -> Core: + """ + Returns the default submodule based on the provided option. + + :param option: Option specifying the submodule + :return: Instance of the corresponding submodule + :raises NotImplementedError: If the option is not recognized + """ + + if option == "QAOA": + from modules.solvers.qaoa import QAOA # pylint: disable=C0415 + return QAOA() + elif option == "PennylaneQAOA": + from modules.solvers.pennylane_qaoa import PennylaneQAOA # pylint: disable=C0415 + return PennylaneQAOA() + elif option == "QiskitQAOA": + from modules.solvers.qiskit_qaoa import QiskitQAOA # pylint: disable=C0415 + return QiskitQAOA() + else: + raise NotImplementedError(f"Solver Option {option} not implemented") diff --git a/src/modules/applications/optimization/bp/mappings/mip.py b/src/modules/applications/optimization/bp/mappings/mip.py new file mode 100644 index 000000000..0cee4edcf --- /dev/null +++ b/src/modules/applications/optimization/bp/mappings/mip.py @@ -0,0 +1,168 @@ +# Copyright 2021 The QUARK Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from typing import TypedDict +import logging + +from qiskit_optimization.translators import from_docplex_mp +from docplex.mp.model import Model +from modules.applications.mapping import Mapping +from modules.core import Core +from utils import start_time_measurement, end_time_measurement + + +class MIP(Mapping): + """ + MIP formulation for the Bin Packing Problem. + """ + + def __init__(self): + """ + Constructor method + """ + super().__init__() + self.submodule_options = ["MIPSolver"] + self.key_mapping = None + self.graph = None + self.config = None + + @staticmethod + def get_requirements() -> list[dict]: + """ + Return requirements of this module. + + :return: List of dict with requirements of this module + """ + return [{"name": "docplex", "version": "2.25.236"}] + + def get_parameter_options(self) -> dict: + """ + Returns the configurable settings for this mapping. + + :return: + .. code-block:: python + + return {} + """ + return {} + + class Config(TypedDict): + """ + Attributes of a valid config. + + .. code-block:: python + + penalty_factor: float + mapping: str + + """ + modelling_goal: float + + def map(self, problem: tuple[list, float, list], config: Config) -> tuple[Model, float]: + """ + Maps the bin packing problem input to a MIP formulation. + + :param problem: Bin packing problem instance defined by + 1. object weights, 2. bin capacity, 3. incompatible objects + :param config: Config with the parameters specified in Config class + :return: Docplex-model, time it took to map it + """ + start = start_time_measurement() + self.problem = problem + self.config = config + + # Create the docplex MIP model + return MIP.create_mip(self, problem), end_time_measurement(start) + + def create_mip(self, problem: tuple[list, float, list]) -> tuple[Model, float]: + """ + Generates a bin-packing problem docplex model depending on a certain instance. + + :param problem: Bin packing problem instance defined by + 1. object weights, 2. bin capacity, 3. incompatible objects + :return: The resulting bin packing model + """ + + # Initialize the problem data + object_weights = problem[0] + bin_capacity = problem[1] + incompatible_objects = problem[2] + + # Create the docplex model + binpacking_mip = Model("BinPacking") + logging.info("Start the creation of the bin-packing-MIP \n") + + # Define the necessary variables for the creation + max_number_of_bins = len(object_weights) + num_of_objects = len(object_weights) + + # Add model variables + bin_variables = binpacking_mip.binary_var_list( + keys=range(max_number_of_bins), name=[ + f"x_{i}" for i in range(max_number_of_bins)]) + + # logging.info("added binary variables x_i --> =1 if bin i is used, =0 if not") + + object_to_bin_variables = binpacking_mip.binary_var_matrix( + keys1=range(num_of_objects), + keys2=range(max_number_of_bins), + name="y") # lambda i,j: f'x{j}{i}') + # logging.info("added binary variables y_j_i --> =1 if object j is put into bin i, =0 if not") + + # Add model objective --> minimize sum of x_i variables + binpacking_mip.minimize(binpacking_mip.sum([bin_variables[i] for i in range(max_number_of_bins)])) + # logging.info("added the objective with goal to minimize") + + # Add model constraints + binpacking_mip.add_constraints((binpacking_mip.sum(object_to_bin_variables[o, i] for i in range( + max_number_of_bins)) == 1 for o in range(num_of_objects)), + ["assignment_constraint_object_%d" % i for i in range(num_of_objects)]) + + logging.info("added constraints so that each object gets assigned to a bin") + + binpacking_mip.add_constraints((binpacking_mip.sum(object_weights[o] * + object_to_bin_variables[o, i] for o in range( + num_of_objects)) <= bin_capacity * bin_variables[i] for i in range(max_number_of_bins)), + ["capacity_constraint_bin_%d" % i for i in range(max_number_of_bins)]) + + # logging.info("added constraints so that the bin-capacity isn't violated") + + # The following is good for the QUBO formulation because we don't need to introduce slack variables + binpacking_mip.add_quadratic_constraints(object_to_bin_variables[o1, i] * + object_to_bin_variables[o2, i] == 0 for ( + o1, o2) in incompatible_objects for i in range(max_number_of_bins)) + # TODO the following is equivalent, but better suited for a MIP Solver because it is linear + # Incompatibility_constraints = binpacking_mip.add_constraints((object_to_bin_variables[o1,i] + + # object_to_bin_variables[o2,i] <= 1 for (o1,o2) in incompatible_objects for i in range(max_number_of_bins)), + # ["incompatibility_constraint_%d" % i for i in range(max_number_of_bins * len(incompatible_objects))]) + + # logging.info("added constraints so that incompatible objects aren't put in the same bin \n") + + logging.info("Finished the creation of the bin-packing-MIP \n\n") + + return binpacking_mip + + def get_default_submodule(self, option: str) -> Core: + """ + Returns the default submodule based on the provided option. + + :param option: Option specifying the submodule + :return: Instance of the corresponding submodule + :raises NotImplementedError: If the option is not recognized + """ + if option == "MIPSolver": + from modules.solvers.mip_solver_bp import MIPSolver # pylint: disable=C0415 + return MIPSolver() + else: + raise NotImplementedError(f"Solver Option {option} not implemented") diff --git a/src/modules/applications/optimization/bp/mappings/qubo.py b/src/modules/applications/optimization/bp/mappings/qubo.py new file mode 100644 index 000000000..559b2fecf --- /dev/null +++ b/src/modules/applications/optimization/bp/mappings/qubo.py @@ -0,0 +1,170 @@ +# Copyright 2021 The QUARK Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from typing import TypedDict +import numpy as np + +from docplex.mp.model import Model + +from modules.applications.mapping import Mapping +from modules.core import Core +from modules.applications.optimization.bp.mappings.mip import MIP + +from qiskit_optimization import QuadraticProgram +from qiskit_optimization.translators import from_docplex_mp +from qiskit_optimization.converters import InequalityToEquality, IntegerToBinary, LinearEqualityToPenalty +from utils import start_time_measurement, end_time_measurement + + +class QUBO(Mapping): + """ + QUBO formulation for the Bin Packing problem. + + """ + + def __init__(self): + """ + Constructor method + """ + super().__init__() + self.submodule_options = ["Annealer"] + + @staticmethod + def get_requirements() -> list[dict]: + """ + Return requirements of this module. + + :return: List of dict with requirements of this module + """ + + return [{"name": "numpy", "version": "1.26.4"}, + {"name": "docplex", "version": "2.25.236"}] + + def get_parameter_options(self) -> dict: + """ + Returns the configurable settings for this mapping. + + :return: + .. code-block:: python + + return { + "penalty_factor": { + "values": [1], + "description": "How do you want to choose your QUBO-penalty-factors?", + "custom_input": True, + "allow_ranges": True, + "postproc": float + } + } + """ + return { + "penalty_factor": { + "values": [1], + "description": "How do you want to choose your QUBO-penalty-factors?", + "custom_input": True, + "allow_ranges": True, + "postproc": float # Since we allow custom input here we need to parse it to float (input is str) + } + } + + class Config(TypedDict): + """ + Attributes of a valid config. + + .. code-block:: python + + penalty_factor: float + + """ + penalty_factor: float + + def map(self, problem: tuple[list, float, list], config: Config) -> tuple[dict, float]: + """ + Maps the bin packing problem input to a QUBO formulation. + + :param problem: Bin packing problem instance defined by + 1. object weights, 2. bin capacity, 3. incompatible objects + :param config: Config with the parameters specified in Config class + :return: Dict with QUBO, time it took to map it + """ + self.problem = problem + self.config = config + start = start_time_measurement() + + # Create docplex model for the binpacking-problem + bin_packing_mip = MIP.create_mip(self, problem) + + # Transform docplex model to QUBO + penalty_factor = config['penalty_factor'] + self.qubo_operator, self.qubo_bin_packing_problem = self.transform_docplex_mip_to_qubo( + bin_packing_mip, penalty_factor + ) + + return { + "Q": self.qubo_operator, + "QUBO": self.qubo_bin_packing_problem + }, end_time_measurement(start) + + def transform_docplex_mip_to_qubo(self, mip_docplex: Model, penalty_factor: float) -> tuple[dict, QuadraticProgram]: + """ + Transform a docplex mixed-integer-problem to a QUBO. + + :param mip_docplex: Docplex-Model + :param penalty_factor: Penalty factor for constraints in QUBO + :return: The transformed QUBO + """ + # Transform docplex model to the qiskit-optimization framework + mip_qiskit = from_docplex_mp(mip_docplex) + + # Transform inequalities to equalities --> with slacks + mip_ineq2eq = InequalityToEquality().convert(mip_qiskit) + + # Transform integer variables to binary variables -->split up into multiple binaries + mip_int2bin = IntegerToBinary().convert(mip_ineq2eq) + + # Transform the linear equality constraints to penalties in the objective + if penalty_factor is None: + # Normalize the coefficients of the QUBO that results from penalty coefficients = 1 + qubo = LinearEqualityToPenalty(penalty=1).convert(mip_int2bin) + max_lin_coeff = numpy.max(abs(qubo.objective.linear.to_array())) + max_quad_coeff = numpy.max(abs(qubo.objective.quadratic.to_array())) + max_coeff = max(max_lin_coeff, max_quad_coeff) + penalty_factor = round(1 / max_coeff, 3) + qubo = LinearEqualityToPenalty(penalty=penalty_factor).convert(mip_int2bin) + + # Squash the quadratic and linear QUBO-coefficients together into a dictionary + quadr_coeff = qubo.objective.quadratic.to_dict(use_name=True) + lin_coeff = qubo.objective.linear.to_dict(use_name=True) + for var, var_value in lin_coeff.items(): + if (var, var) in quadr_coeff.keys(): + quadr_coeff[(var, var)] += var_value + else: + quadr_coeff[(var, var)] = var_value + qubo_operator = quadr_coeff + + return qubo_operator, qubo + + def get_default_submodule(self, option: str) -> Core: + """ + Returns the default submodule based on the provided option. + + :param option: Option specifying the submodule + :return: Instance of the corresponding submodule + :raises NotImplementedError: If the option is not recognized + """ + if option == "Annealer": + from modules.solvers.annealer import Annealer # pylint: disable=C0415 + return Annealer() + else: + raise NotImplementedError(f"Solver Option {option} not implemented") diff --git a/src/modules/applications/optimization/MIS/__init__.py b/src/modules/applications/optimization/mis/__init__.py similarity index 100% rename from src/modules/applications/optimization/MIS/__init__.py rename to src/modules/applications/optimization/mis/__init__.py diff --git a/src/modules/applications/optimization/MIS/data/__init__.py b/src/modules/applications/optimization/mis/data/__init__.py similarity index 100% rename from src/modules/applications/optimization/MIS/data/__init__.py rename to src/modules/applications/optimization/mis/data/__init__.py diff --git a/src/modules/applications/optimization/MIS/data/graph_layouts.py b/src/modules/applications/optimization/mis/data/graph_layouts.py similarity index 100% rename from src/modules/applications/optimization/MIS/data/graph_layouts.py rename to src/modules/applications/optimization/mis/data/graph_layouts.py diff --git a/src/modules/applications/optimization/MIS/mappings/__init__.py b/src/modules/applications/optimization/mis/mappings/__init__.py similarity index 100% rename from src/modules/applications/optimization/MIS/mappings/__init__.py rename to src/modules/applications/optimization/mis/mappings/__init__.py diff --git a/src/modules/applications/optimization/MIS/mappings/NeutralAtom.py b/src/modules/applications/optimization/mis/mappings/neutral_atom.py similarity index 92% rename from src/modules/applications/optimization/MIS/mappings/NeutralAtom.py rename to src/modules/applications/optimization/mis/mappings/neutral_atom.py index edaabf112..f51af4e23 100644 --- a/src/modules/applications/optimization/MIS/mappings/NeutralAtom.py +++ b/src/modules/applications/optimization/mis/mappings/neutral_atom.py @@ -17,8 +17,8 @@ import networkx as nx import pulser -from modules.applications.Mapping import Core, Mapping -from utils import end_time_measurement, start_time_measurement +from modules.applications.mapping import Mapping, Core +from utils import start_time_measurement, end_time_measurement class NeutralAtom(Mapping): @@ -85,7 +85,7 @@ def get_default_submodule(self, option: str) -> Core: :raises NotImplementedError: If the option is not recognized """ if option == "NeutralAtomMIS": - from modules.solvers.NeutralAtomMIS import NeutralAtomMIS # pylint: disable=C0415 + from modules.solvers.neutral_atom_mis import NeutralAtomMIS # pylint: disable=C0415 return NeutralAtomMIS() else: raise NotImplementedError(f"Solver Option {option} not implemented") diff --git a/src/modules/applications/optimization/MIS/mappings/QIRO.py b/src/modules/applications/optimization/mis/mappings/qiro.py similarity index 93% rename from src/modules/applications/optimization/MIS/mappings/QIRO.py rename to src/modules/applications/optimization/mis/mappings/qiro.py index fb5ca8d41..e0e9ee1ab 100644 --- a/src/modules/applications/optimization/MIS/mappings/QIRO.py +++ b/src/modules/applications/optimization/mis/mappings/qiro.py @@ -13,11 +13,10 @@ # limitations under the License. from typing import TypedDict - import networkx -from modules.applications.Mapping import Core, Mapping -from utils import end_time_measurement, start_time_measurement +from modules.applications.mapping import Core, Mapping +from utils import start_time_measurement, end_time_measurement class QIRO(Mapping): @@ -91,7 +90,7 @@ def get_default_submodule(self, option: str) -> Core: :raises NotImplementedError: If the option is not recognized """ if option == "QrispQIRO": - from modules.solvers.QrispQIRO import QIROSolver # pylint: disable=C0415 + from modules.solvers.qrisp_qiro import QIROSolver # pylint: disable=C0415 return QIROSolver() else: raise NotImplementedError(f"Solver Option {option} not implemented") diff --git a/src/modules/applications/optimization/MIS/MIS.py b/src/modules/applications/optimization/mis/mis.py similarity index 96% rename from src/modules/applications/optimization/MIS/MIS.py rename to src/modules/applications/optimization/mis/mis.py index f702ee1e1..5bd4d3176 100644 --- a/src/modules/applications/optimization/MIS/MIS.py +++ b/src/modules/applications/optimization/mis/mis.py @@ -20,9 +20,9 @@ import matplotlib.pyplot as plt from matplotlib.lines import Line2D -from modules.applications.Application import Core -from modules.applications.optimization.Optimization import Optimization -from modules.applications.optimization.MIS.data.graph_layouts import generate_hexagonal_graph +from modules.applications.application import Core +from modules.applications.optimization.optimization import Optimization +from modules.applications.optimization.mis.data.graph_layouts import generate_hexagonal_graph from utils import start_time_measurement, end_time_measurement # Define R_rydberg @@ -82,12 +82,10 @@ def get_default_submodule(self, option: str) -> Core: :raises NotImplementedError: If the option is not recognized """ if option == "QIRO": - from modules.applications.optimization.MIS.mappings.QIRO import \ - QIRO # pylint: disable=C0415 + from modules.applications.optimization.mis.mappings.qiro import QIRO # pylint: disable=C0415 return QIRO() elif option == "NeutralAtom": - from modules.applications.optimization.MIS.mappings.NeutralAtom import \ - NeutralAtom # pylint: disable=C0415 + from modules.applications.optimization.mis.mappings.neutral_atom import NeutralAtom # pylint: disable=C0415 return NeutralAtom() else: raise NotImplementedError(f"Mapping Option {option} not implemented") diff --git a/src/modules/applications/optimization/Optimization.py b/src/modules/applications/optimization/optimization.py similarity index 97% rename from src/modules/applications/optimization/Optimization.py rename to src/modules/applications/optimization/optimization.py index 8d8d7569a..5c87949bd 100644 --- a/src/modules/applications/optimization/Optimization.py +++ b/src/modules/applications/optimization/optimization.py @@ -11,11 +11,11 @@ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. -import logging from abc import ABC, abstractmethod +import logging -from modules.applications.Application import Application -from utils import end_time_measurement, start_time_measurement +from modules.applications.application import Application +from utils import start_time_measurement, end_time_measurement class Optimization(Application, ABC): diff --git a/src/modules/applications/optimization/PVC/__init__.py b/src/modules/applications/optimization/pvc/__init__.py similarity index 100% rename from src/modules/applications/optimization/PVC/__init__.py rename to src/modules/applications/optimization/pvc/__init__.py diff --git a/src/modules/applications/optimization/PVC/data/createReferenceGraph.py b/src/modules/applications/optimization/pvc/data/createReferenceGraph.py similarity index 100% rename from src/modules/applications/optimization/PVC/data/createReferenceGraph.py rename to src/modules/applications/optimization/pvc/data/createReferenceGraph.py diff --git a/src/modules/applications/optimization/PVC/data/reference_data.txt b/src/modules/applications/optimization/pvc/data/reference_data.txt similarity index 100% rename from src/modules/applications/optimization/PVC/data/reference_data.txt rename to src/modules/applications/optimization/pvc/data/reference_data.txt diff --git a/src/modules/applications/optimization/PVC/data/reference_graph.gpickle b/src/modules/applications/optimization/pvc/data/reference_graph.gpickle similarity index 100% rename from src/modules/applications/optimization/PVC/data/reference_graph.gpickle rename to src/modules/applications/optimization/pvc/data/reference_graph.gpickle diff --git a/src/modules/applications/optimization/PVC/mappings/__init__.py b/src/modules/applications/optimization/pvc/mappings/__init__.py similarity index 100% rename from src/modules/applications/optimization/PVC/mappings/__init__.py rename to src/modules/applications/optimization/pvc/mappings/__init__.py diff --git a/src/modules/applications/optimization/PVC/mappings/ISING.py b/src/modules/applications/optimization/pvc/mappings/ising.py similarity index 94% rename from src/modules/applications/optimization/PVC/mappings/ISING.py rename to src/modules/applications/optimization/pvc/mappings/ising.py index c004fdf6f..685746791 100644 --- a/src/modules/applications/optimization/PVC/mappings/ISING.py +++ b/src/modules/applications/optimization/pvc/mappings/ising.py @@ -12,16 +12,16 @@ # See the License for the specific language governing permissions and # limitations under the License. -import logging from typing import TypedDict +import logging import networkx as nx import numpy as np from dimod import qubo_to_ising -from modules.applications.Mapping import Core, Mapping -from modules.applications.optimization.PVC.mappings.QUBO import QUBO -from utils import end_time_measurement, start_time_measurement +from modules.applications.mapping import Mapping, Core +from modules.applications.optimization.pvc.mappings.qubo import QUBO +from utils import start_time_measurement, end_time_measurement class Ising(Mapping): @@ -150,10 +150,10 @@ def get_default_submodule(self, option: str) -> Core: :raises NotImplementedError: If the option is not recognized """ if option == "QAOA": - from modules.solvers.QAOA import QAOA # pylint: disable=C0415 + from modules.solvers.qaoa import QAOA # pylint: disable=C0415 return QAOA() if option == "PennylaneQAOA": - from modules.solvers.PennylaneQAOA import PennylaneQAOA # pylint: disable=C0415 + from modules.solvers.pennylane_qaoa import PennylaneQAOA # pylint: disable=C0415 return PennylaneQAOA() else: raise NotImplementedError(f"Solver Option {option} not implemented") diff --git a/src/modules/applications/optimization/PVC/mappings/QUBO.py b/src/modules/applications/optimization/pvc/mappings/qubo.py similarity index 97% rename from src/modules/applications/optimization/PVC/mappings/QUBO.py rename to src/modules/applications/optimization/pvc/mappings/qubo.py index c7d99266f..c62cf7794 100644 --- a/src/modules/applications/optimization/PVC/mappings/QUBO.py +++ b/src/modules/applications/optimization/pvc/mappings/qubo.py @@ -13,14 +13,14 @@ # limitations under the License. import itertools -import logging from collections import defaultdict from typing import TypedDict +import logging import networkx as nx -from modules.applications.Mapping import Core, Mapping -from utils import end_time_measurement, start_time_measurement +from modules.applications.mapping import Mapping, Core +from utils import start_time_measurement, end_time_measurement class QUBO(Mapping): @@ -195,7 +195,7 @@ def get_default_submodule(self, option: str) -> Core: :raises NotImplementedError: If the option is not recognized """ if option == "Annealer": - from modules.solvers.Annealer import Annealer # pylint: disable=C0415 + from modules.solvers.annealer import Annealer # pylint: disable=C0415 return Annealer() else: raise NotImplementedError(f"Solver Option {option} not implemented") diff --git a/src/modules/applications/optimization/PVC/PVC.py b/src/modules/applications/optimization/pvc/pvc.py similarity index 95% rename from src/modules/applications/optimization/PVC/PVC.py rename to src/modules/applications/optimization/pvc/pvc.py index b15cec673..b4cd862ce 100644 --- a/src/modules/applications/optimization/PVC/PVC.py +++ b/src/modules/applications/optimization/pvc/pvc.py @@ -13,10 +13,10 @@ # limitations under the License. import itertools +from typing import TypedDict +import pickle import logging import os -import pickle -from typing import TypedDict import networkx as nx import matplotlib.pyplot as plt @@ -24,9 +24,9 @@ from matplotlib.patches import Patch import numpy as np -from modules.applications.Application import Core -from modules.applications.optimization.Optimization import Optimization -from utils import end_time_measurement, start_time_measurement +from modules.applications.application import Core +from modules.applications.optimization.optimization import Optimization +from utils import start_time_measurement, end_time_measurement class PVC(Optimization): @@ -84,24 +84,19 @@ def get_default_submodule(self, option: str) -> Core: :raises NotImplementedError: If the option is not recognized """ if option == "Ising": - from modules.applications.optimization.PVC.mappings.ISING import \ - Ising # pylint: disable=C0415 + from modules.applications.optimization.pvc.mappings.ising import Ising # pylint: disable=C0415 return Ising() elif option == "QUBO": - from modules.applications.optimization.PVC.mappings.QUBO import \ - QUBO # pylint: disable=C0415 + from modules.applications.optimization.pvc.mappings.qubo import QUBO # pylint: disable=C0415 return QUBO() elif option == "GreedyClassicalPVC": - from modules.solvers.GreedyClassicalPVC import \ - GreedyClassicalPVC # pylint: disable=C0415 + from modules.solvers.greedy_classical_pvc import GreedyClassicalPVC # pylint: disable=C0415 return GreedyClassicalPVC() elif option == "ReverseGreedyClassicalPVC": - from modules.solvers.ReverseGreedyClassicalPVC import \ - ReverseGreedyClassicalPVC # pylint: disable=C0415 + from modules.solvers.reverse_greedy_classical_pvc import ReverseGreedyClassicalPVC # pylint: disable=C0415 return ReverseGreedyClassicalPVC() elif option == "RandomPVC": - from modules.solvers.RandomClassicalPVC import \ - RandomPVC # pylint: disable=C0415 + from modules.solvers.random_classical_pvc import RandomPVC # pylint: disable=C0415 return RandomPVC() else: raise NotImplementedError(f"Mapping Option {option} not implemented") @@ -235,7 +230,7 @@ def process_solution(self, solution: dict) -> tuple[list, float]: if None in route: logging.info(f"Route until now is: {route}") nodes_unassigned = [(node, 1, 1) for node in nodes if node[0] not in visited_seams] - nodes_unassigned = list(np.random.permutation(nodes_unassigned, dtype=object)) + nodes_unassigned = list(np.random.permutation(nodes_unassigned)) logging.info(nodes_unassigned) logging.info(visited_seams) logging.info(nodes) diff --git a/src/modules/applications/optimization/salbp/__init__.py b/src/modules/applications/optimization/salbp/__init__.py new file mode 100644 index 000000000..4a264f7a6 --- /dev/null +++ b/src/modules/applications/optimization/salbp/__init__.py @@ -0,0 +1 @@ +"""Module for SALBP-1""" diff --git a/src/modules/applications/optimization/salbp/data/example_instance_n=10.alb b/src/modules/applications/optimization/salbp/data/example_instance_n=10.alb new file mode 100644 index 000000000..96422c818 --- /dev/null +++ b/src/modules/applications/optimization/salbp/data/example_instance_n=10.alb @@ -0,0 +1,31 @@ + +10 + + +500 + + +0,3 + + +1 172 +2 73 +3 288 +4 203 +5 152 +6 84 +7 188 +8 105 +9 256 +10 259 + + +1,9 +1,2 +2,4 +3,5 +3,6 +4,10 +5,8 + + diff --git a/src/modules/applications/optimization/salbp/data/example_instance_n=20.alb b/src/modules/applications/optimization/salbp/data/example_instance_n=20.alb new file mode 100644 index 000000000..93aee266e --- /dev/null +++ b/src/modules/applications/optimization/salbp/data/example_instance_n=20.alb @@ -0,0 +1,52 @@ + +20 + + +1000 + + +0,3 + + +1 172 +2 73 +3 288 +4 203 +5 152 +6 84 +7 188 +8 105 +9 256 +10 259 +11 148 +12 172 +13 149 +14 189 +15 59 +16 265 +17 91 +18 61 +19 122 +20 92 + + +1,9 +2,12 +3,6 +3,12 +4,10 +5,8 +6,17 +6,20 +7,14 +8,13 +8,15 +9,11 +9,15 +10,8 +13,6 +14,8 +15,17 +18,10 + + diff --git a/src/modules/applications/optimization/salbp/data/example_instance_n=3.alb b/src/modules/applications/optimization/salbp/data/example_instance_n=3.alb new file mode 100644 index 000000000..777c9dc31 --- /dev/null +++ b/src/modules/applications/optimization/salbp/data/example_instance_n=3.alb @@ -0,0 +1,19 @@ + +3 + + +10 + + +0,3 + + +1 1 +2 7 +3 5 + + +1,2 +1,3 + + diff --git a/src/modules/applications/optimization/salbp/data/example_instance_n=5.alb b/src/modules/applications/optimization/salbp/data/example_instance_n=5.alb new file mode 100644 index 000000000..616e069c2 --- /dev/null +++ b/src/modules/applications/optimization/salbp/data/example_instance_n=5.alb @@ -0,0 +1,22 @@ + +5 + + +10 + + +0,3 + + +1 2 +2 8 +3 4 +4 7 +5 4 + + +1,2 +1,4 +2,3 + + diff --git a/tests/modules/applications/optimization/ACL/__init__.py b/src/modules/applications/optimization/salbp/mappings/__init__.py similarity index 100% rename from tests/modules/applications/optimization/ACL/__init__.py rename to src/modules/applications/optimization/salbp/mappings/__init__.py diff --git a/src/modules/applications/optimization/salbp/mappings/mip.py b/src/modules/applications/optimization/salbp/mappings/mip.py new file mode 100644 index 000000000..7c2ec3c87 --- /dev/null +++ b/src/modules/applications/optimization/salbp/mappings/mip.py @@ -0,0 +1,272 @@ +# Copyright 2021 The QUARK Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +import logging +from typing import TypedDict + +from docplex.mp.dvar import Var +from docplex.mp.model import Model + +from modules.applications.mapping import Mapping +from modules.core import Core +from modules.applications.optimization.salbp.salbp import Task, SALBPInstance +from utils import end_time_measurement, start_time_measurement + + +class MIP(Mapping): + + def __init__(self): + """ + Constructor method + """ + super().__init__() + self.submodule_options = ["MIPSolver"] + self.salbp = None + self.config = None + + @staticmethod + def get_requirements() -> list[dict]: + """ + Return requirements of this module. + + :return: List of dict with requirements of this module + """ + return [{"name": "docplex", "version": "2.25.236"}] + + def get_parameter_options(self) -> dict: + """ + Returns the configurable settings for this mapping. + """ + return {} + + class Config(TypedDict): + """ + Attributes of a valid config. + """ + + def map(self, salbp: SALBPInstance, config: Config) -> tuple[Model, float]: + """ + Map the SALBP-1 instance to its MIP formulation. + + :param salbp: SALBP-1 instance + :param config: Configuration for the mapping + :return: A tuple containing the generated MIP model and the time taken to create it + """ + self.salbp = salbp + start = start_time_measurement() + logging.info("Start the creation of the SALBP-1-MIP") + + max_num_stations: int = salbp.number_of_tasks + # Create the docplex MIP model + salbp_mip: Model = Model("SALBP") + + # Add variables + station_vars, task_station_vars = self._add_variables( + salbp, salbp_mip, max_num_stations + ) + + # Add constraints + self._add_one_station_per_task_constraints( + salbp_mip, salbp.tasks, max_num_stations, task_station_vars + ) + self._add_cycle_time_constraints( + salbp_mip, salbp, max_num_stations, task_station_vars, station_vars + ) + self._add_preceding_tasks_constraints( + salbp_mip, salbp, max_num_stations, task_station_vars + ) + self._add_consecutive_stations_constraints( + salbp_mip, max_num_stations, station_vars + ) + + # Add objective + salbp_mip.minimize(salbp_mip.sum(y for y in station_vars)) + + logging.info("Finished the creation of the SALBP-1-MIP") + + return salbp_mip, end_time_measurement(start) + + @staticmethod + def _add_variables( + salbp: SALBPInstance, + salbp_mip: Model, + max_num_stations: int, + ) -> tuple[list[Var], dict[tuple[int, int], Var]]: + """ + Add two types of binary variables to the SALBP-1 MIP: + - one binary variable y_s per potential station s (y_s = 1 iff s is used) + - one binary variable x_t_s per task-station pair (x_t_s = 1 iff t assigned to s) + + :param salbp: SALBP-1 instance + :param salbp_mip: MIP model for the SALBP-1 instance + :param max_num_stations: Maximum number of stations needed + :return: + - A list of binary variables representing station usage (y_s) + - A dictionary of binary variables representing task assignments (x_t_s) + """ + return ( + salbp_mip.binary_var_list(keys=range(max_num_stations), name="y"), + salbp_mip.binary_var_matrix( + keys1=(task.id for task in salbp.tasks), + keys2=(station for station in range(max_num_stations)), + name=( + f"x_{t.id}_{j}" + for t in salbp.tasks + for j in range(max_num_stations) + ), + ), + ) + + @staticmethod + def _add_one_station_per_task_constraints( + salbp_mip: Model, + tasks: frozenset[Task], + max_num_stations: int, + task_station_vars: dict[tuple[int, int], Var], + ) -> None: + """ + Add constraints to ensure that each task is assigned to exactly one station. + + :param salbp_mip: MIP model for the SALBP-1 instance + :param tasks: Tasks in SALBP-1 instance + :param max_num_stations: Maximum number of stations needed + :param task_station_vars: Binary variables for task-station pairs + """ + salbp_mip.add_constraints( + ( + salbp_mip.sum( + task_station_vars[t.id, s] for s in range(max_num_stations) + ) + == 1 + for t in tasks + ), + names=(f"one_station_per_task_{t.id}" for t in tasks), + ) + + @staticmethod + def _add_cycle_time_constraints( + salbp_mip: Model, + salbp: SALBPInstance, + max_num_stations: int, + task_station_vars: dict[tuple[int, int], Var], + station_vars: list[Var], + ) -> None: + """ + Add constraints to ensure that no station exceeds the cycle time. + + :param salbp_mip: MIP model for the SALBP-1 instance + :param salbp: SALBP-1 instance + :param max_num_stations: Maximum number of stations needed + :param task_station_vars: Binary variables for task-station pairs + :param station_vars: Binary variables for stations + """ + salbp_mip.add_constraints( + ( + salbp_mip.sum( + t.time * task_station_vars[t.id, s] for t in salbp.tasks + ) + <= station_vars[s] * salbp.cycle_time + for s in range(max_num_stations) + ), + names=(f"station_{s}_cycle_time" for s in range(max_num_stations)), + ) + + @staticmethod + def _add_preceding_tasks_constraints( + salbp_mip: Model, + salbp: SALBPInstance, + max_num_stations: int, + task_station_vars: dict[tuple[int, int], Var], + ) -> None: + """ + Add constraints to ensure that preceding tasks are done before their succeeding tasks. + + :param salbp_mip: MIP model for the SALBP-1 instance + :param salbp: SALBP-1 instance + :param max_num_stations: Maximum number of stations needed + :param task_station_vars: Binary variables for task-station pairs + """ + salbp_mip.add_constraints( + ( + salbp_mip.sum( + s * task_station_vars[t1.id, s] for s in range(max_num_stations) + ) + <= salbp_mip.sum( + s * task_station_vars[t2.id, s] for s in range(max_num_stations) + ) + for (t1, t2) in salbp.preceding_tasks + ), + names=( + f"task_{t1.id}_before_task_{t2.id}" + for (t1, t2) in salbp.preceding_tasks + ), + ) + + @staticmethod + def _add_consecutive_stations_constraints( + salbp_mip: Model, max_num_stations: int, station_vars: list[Var] + ) -> None: + """ + Add constraints to ensure that stations are used in consecutive order. This avoids empty stations in between. + + :param salbp_mip: MIP model for the SALBP-1 instance + :param max_num_stations: Maximum number of stations needed + :param station_vars: Binary variables for stations + """ + salbp_mip.add_constraints( + ( + station_vars[s] >= station_vars[s + 1] + for s in range(max_num_stations - 1) + ), + names=(f"consecutive_stations_{s}_{s + 1}" for s in range(max_num_stations)), + ) + + def reverse_map(self, solution: dict[str, int]) -> tuple[dict[int, list[Task]], float]: + """ + Map the solution of the MIP to a task assignment. + + :param solution: A dict mapping a variable name to its solution value + :return: The task assignment and the time it took to create it + """ + start = start_time_measurement() + logging.info( + "Start the reverse mapping of the MIP solution to a task assignment" + ) + used_stations: list[int] = [ + int(var[2:]) + for var, val in solution.items() + if val > 0 and var.startswith("y") + ] + task_assignment: dict[int, list[Task]] = {s: [] for s in used_stations} + for var_name, value in solution.items(): + if var_name.startswith("x") and value > 0: + task, station = var_name.split("_")[1:3] + task_assignment[int(station)].append(self.salbp.get_task(int(task))) + + return task_assignment, end_time_measurement(start) + + def get_default_submodule(self, option: str) -> Core: + """ + Returns the default submodule based on the provided option. + + :param option: The submodule option. + :return: The corresponding submodule instance. + :raises NotImplementedError: If the option is not recognized. + """ + if option == "MIPSolver": + # logging.info(f"Using the module {option} requires the installation of Microsoft Visual C++.") + # logging.info("Please make sure you installed the latest version for your respective system.") + from modules.solvers.mip_solver_bp import MIPSolver # pylint: disable=C0415 + return MIPSolver() + else: + raise NotImplementedError(f"Solver Option {option} not implemented") diff --git a/src/modules/applications/optimization/salbp/salbp.py b/src/modules/applications/optimization/salbp/salbp.py new file mode 100644 index 000000000..8efd8c82a --- /dev/null +++ b/src/modules/applications/optimization/salbp/salbp.py @@ -0,0 +1,487 @@ +# Copyright 2021 The QUARK Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +import itertools +from dataclasses import dataclass, field +from pathlib import Path +from typing import NamedTuple, TypedDict + +import networkx as nx + +from modules.applications.application import Application +from modules.applications.optimization.optimization import Optimization +from modules.core import Core +from utils import end_time_measurement, start_time_measurement + + +# --- TYPINGS --- +TaskId = int +StationId = int + + +class Task(NamedTuple): + """ + A Task for an Assembly Line Balancing Problem. + + Attributes: + id: ID of this task + time: Time that is needed to complete this task + """ + + id: TaskId + time: int + + +TaskAssignment = dict[StationId, list[Task]] + + +@dataclass +class SALBPInstance: + """ + An instance of the Simple Assembly Line Balancing Problem, version 1 (SALBP-1). + + Attributes: + cycle_time: Time that is available for a station + tasks: Tasks in this problem + preceding_tasks: Known tasks' precedence relations + """ + + cycle_time: int + tasks: frozenset[Task] + preceding_tasks: frozenset[tuple[Task, Task]] = field(default_factory=frozenset) + + @property + def number_of_tasks(self) -> int: + """ + Return number of tasks. + + :return: The total number of tasks + """ + return len(self.tasks) + + def get_task(self, task_id: TaskId) -> Task: + """ + Get task for given task_id. + + :param task_id: The ID of the task to retrieve + :return: The corresponding Task object + """ + return next(task for task in self.tasks if task.id == task_id) + + +# --- FACTORY FUNCTION --- +def salbp_factory( + tasks: list[Task], precedence_relations: list[tuple[Task, Task]], cycle_time: int) -> SALBPInstance: + """ + Create an SALBP-1 instance given a list of tasks and their precedence relations. + Do validity checking on the input data and raise a ValueError if the data is invalid. + + :param tasks: The tasks to be assigned to stations + :param precedence_relations: The tasks' precedence relations + :param cycle_time: The cycle time of a station (the same for every station) + :return: An instance of the SALBP-1 + """ + if len(tasks) == 0: + raise ValueError("No tasks registered! Trivial instance (no stations needed).") + + task_ids: list[TaskId] = [task.id for task in tasks] + if not len(task_ids) == len(set(task_ids)): + raise ValueError(f"Some tasks have the same taskId ({tasks})") + + if not all(x >= 0 for x in [task.time for task in tasks]): + raise ValueError(f"Some tasks have a negative task time ({tasks})") + + if not all(task.time <= cycle_time for task in tasks): + raise ValueError(f"Cycle time ({cycle_time}) is too short for some tasks!.") + + task_set = frozenset(tasks) + if not all(t1 in task_set and t2 in task_set for (t1, t2) in precedence_relations): + raise ValueError( + f"Preceding tasks ({precedence_relations}) do not match registered tasks ({tasks})." + ) + + precedence_graph = nx.DiGraph(precedence_relations) + if not nx.is_directed_acyclic_graph(precedence_graph): + raise ValueError("Precedence graph contains cycles!") + + return SALBPInstance( + cycle_time=cycle_time, + tasks=task_set, + preceding_tasks=frozenset(precedence_relations), + ) + + +# --- VALIDITY CHECKS FOR TASK ASSIGNMENT --- +def has_overloaded_station(cycle_time: int, task_assignment: TaskAssignment) -> bool: + """ + Return if a station in the given task_assignment is overloaded wrt the given cycle time. + + :param cycle_time: The maximum time a station can take + :param task_assignment: The assignment of tasks to stations + :return: True if at least one station is overloaded, False otherwise + """ + return any( + sum(task.time for task in tasks) > cycle_time + for tasks in task_assignment.values() + ) + + +def has_unique_assignment_for_every_task( + tasks: frozenset[Task], task_assignment: TaskAssignment +) -> bool: + """ + Return if each task is assigned to exactly one station. + + :param tasks: Set of all tasks in the SALBP-1 instance + :param task_assignment: The assignment of tasks to stations + :return: True if all tasks are uniquely assigned, False otherwise + """ + tasks_in_solution = [task for tasks in task_assignment.values() for task in tasks] + number_of_tasks_correct = len(tasks_in_solution) == len(tasks) + only_unique_tasks = len(set(tasks_in_solution)) == len(tasks_in_solution) + return number_of_tasks_correct and only_unique_tasks + + +def respects_precedences( + preceding_tasks: frozenset[tuple[Task, Task]], task_assignment: TaskAssignment +) -> bool: + """ + Return if the given task_assignment respects the given precedences. + + :param preceding_tasks: Set of precedence constraints between tasks + :param task_assignment: The assignment of tasks to stations + :return: True if all precedence constraints are satisfied, False otherwise + """ + task_to_station_assignment = { + task: station_id + for station_id, tasks in task_assignment.items() + for task in tasks + } + for task_1, task_2 in preceding_tasks: + if task_to_station_assignment[task_1] > task_to_station_assignment[task_2]: + return False + return True + + +# --- PARSING HELPER FUNCTIONS --- +# Parser for Assembly Line Balancing Benchmark Datasets by Otto et al. (2013) +# URL: https://assembly-line-balancing.de/ + +def parse_number_of_tasks(number_task: str) -> int: + """ + Parse the number n of tasks in this problem. + + :param number_task: The number of tasks as a string + :return: The number of tasks as an integer + """ + return int(number_task) + + +def parse_cycle_time(cycle_time: str) -> int: + """ + Parse the available cycle time for one station. + + :param cycle_time: The cycle time as a string + :return: The cycle time as an integer + """ + return int(cycle_time) + + +def parse_order_strength(order_strength: str) -> float: + """ + Parse the order strength of the precedence graph. + + :param order_strength: The order strength as a string + :return: The order strength as a float + """ + return float(order_strength.replace(",", ".")) + + +def parse_task(task_times: str) -> Task: + """ + Parse a task and its time requirement. + + :param task_times: A string containing the task ID and time + :return: A Task instance + """ + task_id, task_time = task_times.split(" ") + return Task(int(task_id), int(task_time)) + + +def parse_precedence_relation(relation: str) -> tuple[TaskId, ...]: + """ + The precedence relations define constraints on the order in which + tasks are performed. A priority relation of task i to task j means + that task i must be completed before task j can be started. + (Task i, Task j). + + :param relation: A string containing task IDs that define precedence constraints + :return: A tuple of task IDs representing precedence constraints + """ + return tuple(int(task) for task in relation.split(",")) + + +TOKEN_PARSER_DISPATCHER = { + "": parse_number_of_tasks, + "": parse_cycle_time, + "": parse_order_strength, + "": parse_task, + "": parse_precedence_relation, + "": None, +} + + +def read_data(file_path: Path) -> list[str]: + """ + Read scenario files in .alb format. + + :param file_path: Path to `.alb` file + :return: List of lines given in data + """ + with open(file=str(file_path), mode="r", encoding="utf-8") as alb_file: + return list( + filter( + lambda s: s != "", list(map(lambda s: s.strip(), alb_file.readlines())) + ) + ) + + +def get_indices(lines: list[str], keywords: list[str]) -> dict[str, int]: + """ + Find the positions of the keywords in the list. + + :param lines: List of lines + :param keywords: Keywords to look for + :return: Dictionary with keyword and their position in lines + """ + return {keyword: lines.index(keyword) for keyword in keywords} + + +def split_lines_to_areas( + lines: list[str], token_indices: dict[str, int] +) -> dict[str, list[str]]: + """ + Group the list into keywords and their corresponding values. + + Each group is introduced by a keyword and ends when another keyword follows. + + :param lines: List of lines + :param token_indices: Keywords and their position in lines + :return: Dictionary with keyword and corresponding values + """ + token_and_range = zip( + token_indices.keys(), itertools.pairwise(token_indices.values()) + ) + return {t: lines[start + 1: stop] for t, (start, stop) in token_and_range} + + +def convert_preceding_task_ids_to_tasks( + tasks: list[Task], preceding_task_ids: list[tuple[TaskId, TaskId]] +) -> list[tuple[Task, Task]]: + """ + Convert a list of preceding task IDs into a list of preceding tasks. + + :param tasks: List of Task objects + :param preceding_task_ids: List of tuples representing task precedence constraints + :return: List of tuples representing task precedence constraints with Task objects + """ + return [ + ( + next(task for task in tasks if task.id == i), + next(task for task in tasks if task.id == j), + ) + for i, j in preceding_task_ids + ] + + +def create_salbp_from_file(file_path: Path) -> SALBPInstance: + """ + Read data from a file and create an SALBP-1 instance. + + :param file_path: Path to the `.alb` file + :return: An instance of SALBP-1 created from the input file + """ + file_content = read_data(file_path) + token_to_index = get_indices(file_content, list(TOKEN_PARSER_DISPATCHER.keys())) + content = split_lines_to_areas(file_content, token_to_index) + content_parsed = {} + for ( + k, + v, + ) in content.items(): + if parser := TOKEN_PARSER_DISPATCHER.get(k): + content_parsed[k] = [parser(vii) for vii in v] + + return salbp_factory( + tasks=content_parsed[""], + precedence_relations=convert_preceding_task_ids_to_tasks( + content_parsed[""], content_parsed[""] + ), + cycle_time=content_parsed[""][0], + ) + + +# --- QUARK OPTIMIZATION APPLICATION --- +class SALBP(Optimization): + """ + The Simple Assembly Line Balancing Problem (SALBP) is a special bin packing problem with precedence relations among + the items. Given a set of tasks, each with a processing time, precedence relations among those tasks, and a cycle + time, this problem's goal is to assign every task to a station s.t. the total number of stations is minimized while + the cycle time per station and the task precedences are respected. This version of the SALBP is commonly referred to + as version 1 (SALBP-1) in the literature. + + The SALBP-1 finds applications in manufacturing, logistics, and industrial automation, where optimizing assembly line + operations can significantly enhance efficiency and cost-effectiveness. By balancing workloads across stations, + industries can minimize idle time, reduce bottlenecks, and improve overall productivity. + + This problem is especially relevant in automobile production, electronics assembly, and large-scale manufacturing, + where tasks must follow strict precedence relations, meaning some tasks must be completed before others can begin. + """ + + def __init__(self): + """ + Constructor method + """ + super().__init__("SALBP") + self.salbp = None + self.task_assignment = None + self.submodule_options = ["MIP"] + + @staticmethod + def get_requirements() -> list: + return [ + {"name": "docplex", "version": "2.25.236"}, + {"name": "networkx", "version": "3.4.2"}, + ] + + def get_solution_quality_unit(self) -> str: + """ + Return the measurement unit for solution quality. + + :return: The unit as a string + """ + return "Number of used stations" + + def get_default_submodule(self, option: str) -> Core: + """ + Returns the default submodule based on the provided option. + + :param option: The submodule option. + :return: The corresponding submodule instance. + :raises NotImplementedError: If the option is not recognized. + """ + if option == "MIP": + from modules.applications.optimization.salbp.mappings.mip import MIP # pylint: disable=C0415 + return MIP() + raise NotImplementedError(f"Mapping Option {option} not implemented") + + def get_parameter_options(self) -> dict: + """ + Return the configurable settings for this application. + + :return: Dictionary containing parameter options + .. code-block:: python + + return { + "instance": { + "values": list( + [ + "example_instance_n=3.alb", + "example_instance_n=5.alb", + "example_instance_n=10.alb", + "example_instance_n=20.alb", + ] + ), + "description": "Which SALBP-1 instance do you want to solve?", + }, + } + """ + return { + "instance": { + "values": list( + [ + "example_instance_n=3.alb", + "example_instance_n=5.alb", + "example_instance_n=10.alb", + "example_instance_n=20.alb", + ] + ), + "description": "Which SALBP-1 instance do you want to solve?", + }, + } + + class Config(TypedDict): + """ + Attributes of a valid config. + """ + + instance: str + + def generate_problem(self, config: Config, **kwargs) -> SALBPInstance: + """ + Generate an SALBP-1 instance using the input configuration. + + :param config: Configuration settings + :return: Generated SALBP-1 instance + """ + if config is None: + config = { + "instance": "example_instance_n=3.alb", + } + + try: + salbp = create_salbp_from_file( + file_path=Path(__file__).parent / "data" / config["instance"] + ) + self.salbp = salbp + except ValueError as err: + raise err + + return self.salbp + + def validate(self, solution: dict[int, list[Task]]) -> tuple[bool, float]: + """ + Validate if a given solution is feasible for the problem instance. + + :param solution: The task assignment + :return: Whether the solution is valid, time it took to validate + """ + start = start_time_measurement() + + if solution is None: + return False, end_time_measurement(start) + self.task_assignment = solution + + return ( + not has_overloaded_station(self.salbp.cycle_time, self.task_assignment) + and has_unique_assignment_for_every_task(self.salbp.tasks, self.task_assignment) + and respects_precedences(self.salbp.preceding_tasks, self.task_assignment) + ), end_time_measurement(start) + + def evaluate(self, solution: dict[int, list[Task]]) -> tuple[float, float]: + """ + Determine objective value of the solution, i.e., the number of used stations. + + :param solution: The task assignment + :return: Objective value, time it took to evaluate + """ + start = start_time_measurement() + + if solution is None: + return False, end_time_measurement(start) + obj_value = len([s for s in self.task_assignment.values() if len(s) > 0]) + + return obj_value, end_time_measurement(start) + + def save(self, path: str, iter_count: int) -> None: + pass diff --git a/src/modules/applications/optimization/SAT/__init__.py b/src/modules/applications/optimization/sat/__init__.py similarity index 100% rename from src/modules/applications/optimization/SAT/__init__.py rename to src/modules/applications/optimization/sat/__init__.py diff --git a/src/modules/applications/optimization/SAT/mappings/__init__.py b/src/modules/applications/optimization/sat/mappings/__init__.py similarity index 100% rename from src/modules/applications/optimization/SAT/mappings/__init__.py rename to src/modules/applications/optimization/sat/mappings/__init__.py diff --git a/src/modules/applications/optimization/SAT/mappings/ChoiISING.py b/src/modules/applications/optimization/sat/mappings/choiising.py similarity index 93% rename from src/modules/applications/optimization/SAT/mappings/ChoiISING.py rename to src/modules/applications/optimization/sat/mappings/choiising.py index 0788c19b4..62639ebe9 100644 --- a/src/modules/applications/optimization/SAT/mappings/ChoiISING.py +++ b/src/modules/applications/optimization/sat/mappings/choiising.py @@ -17,9 +17,9 @@ import numpy as np from dimod import qubo_to_ising -from modules.applications.Mapping import Core, Mapping -from modules.applications.optimization.SAT.mappings.ChoiQUBO import ChoiQUBO -from utils import end_time_measurement, start_time_measurement +from modules.applications.mapping import Mapping, Core +from modules.applications.optimization.sat.mappings.choiqubo import ChoiQUBO +from utils import start_time_measurement, end_time_measurement class ChoiIsing(Mapping): @@ -147,10 +147,10 @@ def get_default_submodule(self, option: str) -> Core: :raises NotImplementedError: If the option is not recognized """ if option == "QAOA": - from modules.solvers.QAOA import QAOA # pylint: disable=C0415 + from modules.solvers.qaoa import QAOA # pylint: disable=C0415 return QAOA() if option == "PennylaneQAOA": - from modules.solvers.PennylaneQAOA import PennylaneQAOA # pylint: disable=C0415 + from modules.solvers.pennylane_qaoa import PennylaneQAOA # pylint: disable=C0415 return PennylaneQAOA() else: raise NotImplementedError(f"Solver Option {option} not implemented") diff --git a/src/modules/applications/optimization/SAT/mappings/ChoiQUBO.py b/src/modules/applications/optimization/sat/mappings/choiqubo.py similarity index 98% rename from src/modules/applications/optimization/SAT/mappings/ChoiQUBO.py rename to src/modules/applications/optimization/sat/mappings/choiqubo.py index 802376494..2a1651a54 100644 --- a/src/modules/applications/optimization/SAT/mappings/ChoiQUBO.py +++ b/src/modules/applications/optimization/sat/mappings/choiqubo.py @@ -12,14 +12,14 @@ # See the License for the specific language governing permissions and # limitations under the License. -import logging from itertools import combinations, product from typing import TypedDict +import logging -from nnf import And, Var +from nnf import Var, And -from modules.applications.Mapping import Core, Mapping -from utils import end_time_measurement, start_time_measurement +from modules.applications.mapping import Mapping, Core +from utils import start_time_measurement, end_time_measurement class ChoiQUBO(Mapping): @@ -244,7 +244,7 @@ def get_default_submodule(self, option: str) -> Core: :raises NotImplementedError: If the option is not recognized """ if option == "Annealer": - from modules.solvers.Annealer import Annealer # pylint: disable=C0415 + from modules.solvers.annealer import Annealer # pylint: disable=C0415 return Annealer() else: raise NotImplementedError(f"Solver Option {option} not implemented") diff --git a/src/modules/applications/optimization/SAT/mappings/DinneenISING.py b/src/modules/applications/optimization/sat/mappings/dinneenising.py similarity index 94% rename from src/modules/applications/optimization/SAT/mappings/DinneenISING.py rename to src/modules/applications/optimization/sat/mappings/dinneenising.py index acd0156c5..89e58672d 100644 --- a/src/modules/applications/optimization/SAT/mappings/DinneenISING.py +++ b/src/modules/applications/optimization/sat/mappings/dinneenising.py @@ -18,8 +18,8 @@ from dimod import qubo_to_ising from nnf import And -from modules.applications.Mapping import Mapping, Core -from modules.applications.optimization.SAT.mappings.DinneenQUBO import DinneenQUBO +from modules.applications.mapping import Mapping, Core +from modules.applications.optimization.sat.mappings.dinneenqubo import DinneenQUBO from utils import start_time_measurement, end_time_measurement @@ -139,11 +139,10 @@ def get_default_submodule(self, option: str) -> Core: :raises NotImplementedError: If the option is not recognized """ if option == "QAOA": - from modules.solvers.QAOA import QAOA # pylint: disable=C0415 + from modules.solvers.qaoa import QAOA # pylint: disable=C0415 return QAOA() if option == "PennylaneQAOA": - from modules.solvers.PennylaneQAOA import \ - PennylaneQAOA # pylint: disable=C0415 + from modules.solvers.pennylane_qaoa import PennylaneQAOA # pylint: disable=C0415 return PennylaneQAOA() else: raise NotImplementedError(f"Solver Option {option} not implemented") diff --git a/src/modules/applications/optimization/SAT/mappings/DinneenQUBO.py b/src/modules/applications/optimization/sat/mappings/dinneenqubo.py similarity index 97% rename from src/modules/applications/optimization/SAT/mappings/DinneenQUBO.py rename to src/modules/applications/optimization/sat/mappings/dinneenqubo.py index d8edc32c8..791e30f9a 100644 --- a/src/modules/applications/optimization/SAT/mappings/DinneenQUBO.py +++ b/src/modules/applications/optimization/sat/mappings/dinneenqubo.py @@ -12,14 +12,14 @@ # See the License for the specific language governing permissions and # limitations under the License. -import logging from itertools import combinations from typing import TypedDict +import logging from nnf import And -from modules.applications.Mapping import Core, Mapping -from utils import end_time_measurement, start_time_measurement +from modules.applications.mapping import Mapping, Core +from utils import start_time_measurement, end_time_measurement class DinneenQUBO(Mapping): @@ -192,7 +192,7 @@ def get_default_submodule(self, option: str) -> Core: :raises NotImplementedError: If the option is not recognized """ if option == "Annealer": - from modules.solvers.Annealer import Annealer # pylint: disable=C0415 + from modules.solvers.annealer import Annealer # pylint: disable=C0415 return Annealer() else: raise NotImplementedError(f"Solver Option {option} not implemented") diff --git a/src/modules/applications/optimization/SAT/mappings/Direct.py b/src/modules/applications/optimization/sat/mappings/direct.py similarity index 93% rename from src/modules/applications/optimization/SAT/mappings/Direct.py rename to src/modules/applications/optimization/sat/mappings/direct.py index 4fc286723..85b874c49 100644 --- a/src/modules/applications/optimization/SAT/mappings/Direct.py +++ b/src/modules/applications/optimization/sat/mappings/direct.py @@ -13,15 +13,15 @@ # limitations under the License. import io -import logging from typing import TypedDict +import logging from nnf import And from nnf.dimacs import dump from pysat.formula import CNF, WCNF -from modules.applications.Mapping import Core, Mapping -from utils import end_time_measurement, start_time_measurement +from modules.applications.mapping import Mapping, Core +from utils import start_time_measurement, end_time_measurement class Direct(Mapping): @@ -119,10 +119,10 @@ def get_default_submodule(self, option: str) -> Core: :raises NotImplementedError: If the option is not recognized """ if option == "ClassicalSAT": - from modules.solvers.ClassicalSAT import ClassicalSAT # pylint: disable=C0415 + from modules.solvers.classical_sat import ClassicalSAT # pylint: disable=C0415 return ClassicalSAT() elif option == "RandomSAT": - from modules.solvers.RandomClassicalSAT import RandomSAT # pylint: disable=C0415 + from modules.solvers.random_classical_sat import RandomSAT # pylint: disable=C0415 return RandomSAT() else: raise NotImplementedError(f"Solver Option {option} not implemented") diff --git a/src/modules/applications/optimization/SAT/mappings/QubovertQUBO.py b/src/modules/applications/optimization/sat/mappings/qubovertqubo.py similarity index 96% rename from src/modules/applications/optimization/SAT/mappings/QubovertQUBO.py rename to src/modules/applications/optimization/sat/mappings/qubovertqubo.py index 28a56839b..3b7e22506 100644 --- a/src/modules/applications/optimization/SAT/mappings/QubovertQUBO.py +++ b/src/modules/applications/optimization/sat/mappings/qubovertqubo.py @@ -15,11 +15,11 @@ import logging from typing import TypedDict +from qubovert.sat import NOT, OR, AND from nnf import And -from qubovert.sat import AND, NOT, OR -from modules.applications.Mapping import Core, Mapping -from utils import end_time_measurement, start_time_measurement +from modules.applications.mapping import Mapping, Core +from utils import start_time_measurement, end_time_measurement class QubovertQUBO(Mapping): @@ -189,7 +189,7 @@ def get_default_submodule(self, option: str) -> Core: :raises NotImplementedError: If the option is not recognized """ if option == "Annealer": - from modules.solvers.Annealer import Annealer # pylint: disable=C0415 + from modules.solvers.annealer import Annealer # pylint: disable=C0415 return Annealer() else: raise NotImplementedError(f"Solver Option {option} not implemented") diff --git a/src/modules/applications/optimization/SAT/SAT.py b/src/modules/applications/optimization/sat/sat.py similarity index 94% rename from src/modules/applications/optimization/SAT/SAT.py rename to src/modules/applications/optimization/sat/sat.py index fd8fa4e03..eb94a270a 100644 --- a/src/modules/applications/optimization/SAT/SAT.py +++ b/src/modules/applications/optimization/sat/sat.py @@ -17,12 +17,12 @@ import nnf import numpy as np -from nnf import And, Or, Var +from nnf import Var, And, Or from nnf.dimacs import dump -from modules.applications.optimization.Optimization import Optimization -from modules.Core import Core -from utils import end_time_measurement, start_time_measurement +from modules.core import Core +from modules.applications.optimization.optimization import Optimization +from utils import start_time_measurement, end_time_measurement class SAT(Optimization): @@ -88,27 +88,23 @@ def get_default_submodule(self, option: str) -> Core: :raises NotImplementedError: If the option is not recognized """ if option == "QubovertQUBO": - from modules.applications.optimization.SAT.mappings.QubovertQUBO import \ + from modules.applications.optimization.sat.mappings.qubovertqubo import \ QubovertQUBO # pylint: disable=C0415 return QubovertQUBO() elif option == "Direct": - from modules.applications.optimization.SAT.mappings.Direct import \ - Direct # pylint: disable=C0415 + from modules.applications.optimization.sat.mappings.direct import Direct # pylint: disable=C0415 return Direct() elif option == "ChoiQUBO": - from modules.applications.optimization.SAT.mappings.ChoiQUBO import \ - ChoiQUBO # pylint: disable=C0415 + from modules.applications.optimization.sat.mappings.choiqubo import ChoiQUBO # pylint: disable=C0415 return ChoiQUBO() elif option == "ChoiIsing": - from modules.applications.optimization.SAT.mappings.ChoiISING import \ - ChoiIsing # pylint: disable=C0415 + from modules.applications.optimization.sat.mappings.choiising import ChoiIsing # pylint: disable=C0415 return ChoiIsing() elif option == "DinneenQUBO": - from modules.applications.optimization.SAT.mappings.DinneenQUBO import \ - DinneenQUBO # pylint: disable=C0415 + from modules.applications.optimization.sat.mappings.dinneenqubo import DinneenQUBO # pylint: disable=C0415 return DinneenQUBO() elif option == "DinneenIsing": - from modules.applications.optimization.SAT.mappings.DinneenISING import \ + from modules.applications.optimization.sat.mappings.dinneenising import \ DinneenIsing # pylint: disable=C0415 return DinneenIsing() else: diff --git a/src/modules/applications/optimization/SCP/__init__.py b/src/modules/applications/optimization/scp/__init__.py similarity index 100% rename from src/modules/applications/optimization/SCP/__init__.py rename to src/modules/applications/optimization/scp/__init__.py diff --git a/src/modules/applications/optimization/SCP/data/__init__.py b/src/modules/applications/optimization/scp/data/__init__.py similarity index 100% rename from src/modules/applications/optimization/SCP/data/__init__.py rename to src/modules/applications/optimization/scp/data/__init__.py diff --git a/src/modules/applications/optimization/SCP/data/set_cover_data_large.txt b/src/modules/applications/optimization/scp/data/set_cover_data_large.txt similarity index 100% rename from src/modules/applications/optimization/SCP/data/set_cover_data_large.txt rename to src/modules/applications/optimization/scp/data/set_cover_data_large.txt diff --git a/src/modules/applications/optimization/SCP/mappings/__init__.py b/src/modules/applications/optimization/scp/mappings/__init__.py similarity index 100% rename from src/modules/applications/optimization/SCP/mappings/__init__.py rename to src/modules/applications/optimization/scp/mappings/__init__.py diff --git a/src/modules/applications/optimization/SCP/mappings/qubovertQUBO.py b/src/modules/applications/optimization/scp/mappings/qubovertqubo.py similarity index 96% rename from src/modules/applications/optimization/SCP/mappings/qubovertQUBO.py rename to src/modules/applications/optimization/scp/mappings/qubovertqubo.py index 5c0aaf890..6e8520182 100644 --- a/src/modules/applications/optimization/SCP/mappings/qubovertQUBO.py +++ b/src/modules/applications/optimization/scp/mappings/qubovertqubo.py @@ -16,9 +16,8 @@ from typing import TypedDict from qubovert.problems import SetCover - -from modules.applications.Mapping import Core, Mapping -from utils import end_time_measurement, start_time_measurement +from modules.applications.mapping import Mapping, Core +from utils import start_time_measurement, end_time_measurement class QubovertQUBO(Mapping): @@ -140,7 +139,7 @@ def get_default_submodule(self, option: str) -> Core: :raises NotImplementedError: If the option is not recognized """ if option == "Annealer": - from modules.solvers.Annealer import Annealer # pylint: disable=C0415 + from modules.solvers.annealer import Annealer # pylint: disable=C0415 return Annealer() else: raise NotImplementedError(f"Solver Option {option} not implemented") diff --git a/src/modules/applications/optimization/SCP/SCP.py b/src/modules/applications/optimization/scp/scp.py similarity index 96% rename from src/modules/applications/optimization/SCP/SCP.py rename to src/modules/applications/optimization/scp/scp.py index 7e0eb2809..cf533dbb4 100644 --- a/src/modules/applications/optimization/SCP/SCP.py +++ b/src/modules/applications/optimization/scp/scp.py @@ -12,13 +12,13 @@ # See the License for the specific language governing permissions and # limitations under the License. -import os -import pickle from typing import TypedDict +import pickle +import os -from modules.applications.Application import Application -from modules.applications.optimization.Optimization import Optimization -from utils import end_time_measurement, start_time_measurement +from modules.applications.application import Application +from modules.applications.optimization.optimization import Optimization +from utils import start_time_measurement, end_time_measurement class SCP(Optimization): @@ -60,7 +60,7 @@ def get_default_submodule(self, option: str) -> Application: :raises NotImplementedError: If the option is not recognized """ if option == "qubovertQUBO": - from modules.applications.optimization.SCP.mappings.qubovertQUBO import QubovertQUBO # pylint: disable=C0415 + from modules.applications.optimization.scp.mappings.qubovertqubo import QubovertQUBO # pylint: disable=C0415 return QubovertQUBO() else: raise NotImplementedError(f"Mapping Option {option} not implemented") diff --git a/src/modules/applications/optimization/TSP/__init__.py b/src/modules/applications/optimization/tsp/__init__.py similarity index 100% rename from src/modules/applications/optimization/TSP/__init__.py rename to src/modules/applications/optimization/tsp/__init__.py diff --git a/src/modules/applications/optimization/TSP/data/createReferenceGraph.py b/src/modules/applications/optimization/tsp/data/createReferenceGraph.py similarity index 100% rename from src/modules/applications/optimization/TSP/data/createReferenceGraph.py rename to src/modules/applications/optimization/tsp/data/createReferenceGraph.py diff --git a/src/modules/applications/optimization/TSP/data/dsj1000.tsp b/src/modules/applications/optimization/tsp/data/dsj1000.tsp similarity index 100% rename from src/modules/applications/optimization/TSP/data/dsj1000.tsp rename to src/modules/applications/optimization/tsp/data/dsj1000.tsp diff --git a/src/modules/applications/optimization/TSP/data/reference_graph.gpickle b/src/modules/applications/optimization/tsp/data/reference_graph.gpickle similarity index 100% rename from src/modules/applications/optimization/TSP/data/reference_graph.gpickle rename to src/modules/applications/optimization/tsp/data/reference_graph.gpickle diff --git a/src/modules/applications/optimization/TSP/mappings/__init__.py b/src/modules/applications/optimization/tsp/mappings/__init__.py similarity index 100% rename from src/modules/applications/optimization/TSP/mappings/__init__.py rename to src/modules/applications/optimization/tsp/mappings/__init__.py diff --git a/src/modules/applications/optimization/TSP/mappings/ISING.py b/src/modules/applications/optimization/tsp/mappings/ising.py similarity index 95% rename from src/modules/applications/optimization/TSP/mappings/ISING.py rename to src/modules/applications/optimization/tsp/mappings/ising.py index 7e8d15195..3b4d01fa5 100644 --- a/src/modules/applications/optimization/TSP/mappings/ISING.py +++ b/src/modules/applications/optimization/tsp/mappings/ising.py @@ -12,21 +12,20 @@ # See the License for the specific language governing permissions and # limitations under the License. -import logging import re from typing import TypedDict +import logging import networkx as nx import numpy as np from dimod import qubo_to_ising from more_itertools import locate - from qiskit_optimization.applications import Tsp from qiskit_optimization.converters import QuadraticProgramToQubo -from modules.applications.Mapping import Core, Mapping -from modules.applications.optimization.TSP.mappings.QUBO import QUBO -from utils import end_time_measurement, start_time_measurement +from modules.applications.mapping import Mapping, Core +from modules.applications.optimization.tsp.mappings.qubo import QUBO +from utils import start_time_measurement, end_time_measurement class Ising(Mapping): @@ -262,13 +261,13 @@ def get_default_submodule(self, option: str) -> Core: :raises NotImplemented: If the provided option is not implemented """ if option == "QAOA": - from modules.solvers.QAOA import QAOA # pylint: disable=C0415 + from modules.solvers.qaoa import QAOA # pylint: disable=C0415 return QAOA() elif option == "PennylaneQAOA": - from modules.solvers.PennylaneQAOA import PennylaneQAOA # pylint: disable=C0415 + from modules.solvers.pennylane_qaoa import PennylaneQAOA # pylint: disable=C0415 return PennylaneQAOA() elif option == "QiskitQAOA": - from modules.solvers.QiskitQAOA import QiskitQAOA # pylint: disable=C0415 + from modules.solvers.qiskit_qaoa import QiskitQAOA # pylint: disable=C0415 return QiskitQAOA() else: raise NotImplementedError(f"Solver Option {option} not implemented") diff --git a/src/modules/applications/optimization/TSP/mappings/QUBO.py b/src/modules/applications/optimization/tsp/mappings/qubo.py similarity index 95% rename from src/modules/applications/optimization/TSP/mappings/QUBO.py rename to src/modules/applications/optimization/tsp/mappings/qubo.py index fde80e133..59eaa8319 100644 --- a/src/modules/applications/optimization/TSP/mappings/QUBO.py +++ b/src/modules/applications/optimization/tsp/mappings/qubo.py @@ -12,14 +12,14 @@ # See the License for the specific language governing permissions and # limitations under the License. -import logging from typing import TypedDict +import logging import dwave_networkx as dnx import networkx -from modules.applications.Mapping import Core, Mapping -from utils import end_time_measurement, start_time_measurement +from modules.applications.mapping import Mapping, Core +from utils import start_time_measurement, end_time_measurement class QUBO(Mapping): @@ -119,7 +119,7 @@ def get_default_submodule(self, option: str) -> Core: """ if option == "Annealer": - from modules.solvers.Annealer import Annealer # pylint: disable=C0415 + from modules.solvers.annealer import Annealer # pylint: disable=C0415 return Annealer() else: raise NotImplementedError(f"Solver Option {option} not implemented") diff --git a/src/modules/applications/optimization/TSP/TSP.py b/src/modules/applications/optimization/tsp/tsp.py similarity index 94% rename from src/modules/applications/optimization/TSP/TSP.py rename to src/modules/applications/optimization/tsp/tsp.py index 35146241a..cab27b28b 100644 --- a/src/modules/applications/optimization/TSP/TSP.py +++ b/src/modules/applications/optimization/tsp/tsp.py @@ -12,18 +12,18 @@ # See the License for the specific language governing permissions and # limitations under the License. +from typing import TypedDict +import pickle import logging import os -import pickle -from typing import TypedDict import networkx as nx import matplotlib.pyplot as plt import numpy as np -from modules.applications.Application import Core -from modules.applications.optimization.Optimization import Optimization -from utils import end_time_measurement, start_time_measurement +from modules.applications.application import Core +from modules.applications.optimization.optimization import Optimization +from utils import start_time_measurement, end_time_measurement class TSP(Optimization): @@ -78,19 +78,19 @@ def get_default_submodule(self, option: str) -> Core: :raises NotImplemented: If the provided option is not implemented """ if option == "Ising": - from modules.applications.optimization.TSP.mappings.ISING import Ising # pylint: disable=C0415 + from modules.applications.optimization.tsp.mappings.ising import Ising # pylint: disable=C0415 return Ising() elif option == "QUBO": - from modules.applications.optimization.TSP.mappings.QUBO import QUBO # pylint: disable=C0415 + from modules.applications.optimization.tsp.mappings.qubo import QUBO # pylint: disable=C0415 return QUBO() elif option == "GreedyClassicalTSP": - from modules.solvers.GreedyClassicalTSP import GreedyClassicalTSP # pylint: disable=C0415 + from modules.solvers.greedy_classical_tsp import GreedyClassicalTSP # pylint: disable=C0415 return GreedyClassicalTSP() elif option == "ReverseGreedyClassicalTSP": - from modules.solvers.ReverseGreedyClassicalTSP import ReverseGreedyClassicalTSP # pylint: disable=C0415 + from modules.solvers.reverse_greedy_classical_tsp import ReverseGreedyClassicalTSP # pylint: disable=C0415 return ReverseGreedyClassicalTSP() elif option == "RandomTSP": - from modules.solvers.RandomClassicalTSP import RandomTSP # pylint: disable=C0415 + from modules.solvers.random_classical_tsp import RandomTSP # pylint: disable=C0415 return RandomTSP() else: raise NotImplementedError(f"Mapping Option {option} not implemented") diff --git a/src/modules/applications/qml/Circuit.py b/src/modules/applications/qml/circuit.py similarity index 97% rename from src/modules/applications/qml/Circuit.py rename to src/modules/applications/qml/circuit.py index cc44cd10c..7ff2cf6c6 100644 --- a/src/modules/applications/qml/Circuit.py +++ b/src/modules/applications/qml/circuit.py @@ -13,8 +13,7 @@ # limitations under the License. from abc import ABC, abstractmethod - -from modules.Core import Core +from modules.core import Core class Circuit(Core, ABC): diff --git a/src/modules/applications/qml/classification/classification.py b/src/modules/applications/qml/classification/classification.py new file mode 100644 index 000000000..bda6c548d --- /dev/null +++ b/src/modules/applications/qml/classification/classification.py @@ -0,0 +1,140 @@ +# Copyright 2021 The QUARK Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from typing import TypedDict + +# from modules.applications.Application import * +from modules.applications.qml.classification.data.data_handler.image_data import ImageData +from modules.applications.qml.qml import QML +from utils import end_time_measurement, start_time_measurement + + +class Classification(QML): + """ + Image Classification using quantum circuits. + """ + + def __init__(self): + """ + Constructor method. + """ + super().__init__("Classification") + self.submodule_options = ["Image Data"] + self.data = None + + @staticmethod + def get_requirements() -> list[dict]: + """ + Returns requirements of this module. + + :return: list of dict with requirements of this module + """ + # These requirements are used in the accompanying module QuantumModel + return [ + {"name": "numpy", "version": "1.26.4"}, + {"name": "torch", "version": "2.2.2"}, + {"name": "pennylane", "version": "0.39.0"}, + {"name": "qiskit", "version": "1.3.0"}, + {"name": "qiskit-machine-learning", "version": "0.8.2"}, + {"name": "torchvision", "version": "0.17.2"}, + ] + + def get_solution_quality_unit(self) -> str: + return "maximal Accuracy" + + def get_default_submodule(self, option: str) -> ImageData: + """ + Returns the default submodule based on the given option. + + :param option: The submodule option to select + :return: Instance of the selected submodule + :raises NotImplemented: If the provided option is not implemented + """ + if option == "Image Data": + self.data = ImageData() + else: + raise NotImplementedError(f"Transformation Option {option} not implemented") + return self.data + + def get_parameter_options(self) -> dict: + """ + Returns the configurable settings for this application + + :return: Dictionary of configurable parameters + .. code-block:: python + + return { + "n_qubits": { + "values": [4, 6], + "description": "How many qubits do you want to use?" + } + } + + """ + return { + "n_qubits": { + "values": [4, 6], + "description": "How many qubits do you want to use?", + } + } + + class Config(TypedDict): + """ + Attributes of a valid config + + .. code-block:: python + + n_qubits: int + """ + + n_qubits: int + + def generate_problem(self, config: dict) -> dict: + """ + The number of qubits is chosen for this problem. + + :param config: + :return: Dictionary with the number of qubits + """ + + application_config = {"n_qubits": config["n_qubits"]} + return application_config + + def preprocess(self, input_data: dict, config: dict, **kwargs: dict) -> tuple[dict, float]: + """ + Generate the actual problem instance in the preprocess function. + + :param input_data: Usually not used for this method + :param config: Config for the problem creation + :param kwargs: Optional additional arguments + + :return: Tuple containing qubit number and the function's computation time + """ + start = start_time_measurement() + output = self.generate_problem(config) + output["store_dir_iter"] = f"{kwargs['store_dir']}/rep_{kwargs['rep_count']}" + return output, end_time_measurement(start) + + def postprocess(self, input_data: dict, config: dict, **kwargs: dict) -> tuple[dict, float]: + """ + Process the solution here, then validate and evaluate it. + + :param input_data: Representation of the quantum machine learning model that will be trained + :param config: Config specifying the parameters of the training + :param kwargs: Optional keyword arguments + :return: Tuple with same dictionary like input_data and the time + """ + + start = start_time_measurement() + return input_data, end_time_measurement(start) diff --git a/src/modules/applications/qml/classification/data/Images_2D/.gitattributes b/src/modules/applications/qml/classification/data/Images_2D/.gitattributes new file mode 100644 index 000000000..bc499e5d6 --- /dev/null +++ b/src/modules/applications/qml/classification/data/Images_2D/.gitattributes @@ -0,0 +1,3 @@ +*.gz filter=lfs diff=lfs merge=lfs -text +*.jpg filter=lfs diff=lfs merge=lfs -text +*.jpeg filter=lfs diff=lfs merge=lfs -text diff --git a/src/modules/applications/qml/classification/data/Images_2D/mnist/raw/t10k-images-idx3-ubyte b/src/modules/applications/qml/classification/data/Images_2D/mnist/raw/t10k-images-idx3-ubyte new file mode 100644 index 000000000..1170b2cae Binary files /dev/null and b/src/modules/applications/qml/classification/data/Images_2D/mnist/raw/t10k-images-idx3-ubyte differ diff --git 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b/src/modules/applications/qml/classification/data/Images_2D/mnist/raw/t10k-labels-idx1-ubyte.gz new file mode 100644 index 000000000..d1995bebe --- /dev/null +++ b/src/modules/applications/qml/classification/data/Images_2D/mnist/raw/t10k-labels-idx1-ubyte.gz @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:f7ae60f92e00ec6debd23a6088c31dbd2371eca3ffa0defaefb259924204aec6 +size 4542 diff --git a/src/modules/applications/qml/classification/data/Images_2D/mnist/raw/train-images-idx3-ubyte b/src/modules/applications/qml/classification/data/Images_2D/mnist/raw/train-images-idx3-ubyte new file mode 100644 index 000000000..bbce27659 Binary files /dev/null and b/src/modules/applications/qml/classification/data/Images_2D/mnist/raw/train-images-idx3-ubyte differ diff --git a/src/modules/applications/qml/classification/data/Images_2D/mnist/raw/train-images-idx3-ubyte.gz b/src/modules/applications/qml/classification/data/Images_2D/mnist/raw/train-images-idx3-ubyte.gz new file mode 100644 index 000000000..9e9852c14 --- /dev/null +++ b/src/modules/applications/qml/classification/data/Images_2D/mnist/raw/train-images-idx3-ubyte.gz @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:440fcabf73cc546fa21475e81ea370265605f56be210a4024d2ca8f203523609 +size 9912422 diff --git a/src/modules/applications/qml/classification/data/Images_2D/mnist/raw/train-labels-idx1-ubyte b/src/modules/applications/qml/classification/data/Images_2D/mnist/raw/train-labels-idx1-ubyte new file mode 100644 index 000000000..d6b4c5db3 Binary files /dev/null and b/src/modules/applications/qml/classification/data/Images_2D/mnist/raw/train-labels-idx1-ubyte differ diff --git a/src/modules/applications/qml/classification/data/Images_2D/mnist/raw/train-labels-idx1-ubyte.gz b/src/modules/applications/qml/classification/data/Images_2D/mnist/raw/train-labels-idx1-ubyte.gz new file mode 100644 index 000000000..a7ebf9b5b --- /dev/null +++ b/src/modules/applications/qml/classification/data/Images_2D/mnist/raw/train-labels-idx1-ubyte.gz @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:3552534a0a558bbed6aed32b30c495cca23d567ec52cac8be1a0730e8010255c +size 28881 diff --git a/src/modules/applications/qml/classification/data/Images_2D/test_data/Negative/Negative.zip b/src/modules/applications/qml/classification/data/Images_2D/test_data/Negative/Negative.zip new file mode 100644 index 000000000..491465705 --- /dev/null +++ b/src/modules/applications/qml/classification/data/Images_2D/test_data/Negative/Negative.zip @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:20f9748c27cd5eb7b2d030c35b55962b61bc9f7a80bdbcc5c64c763eb63b00d6 +size 108541814 diff --git a/src/modules/applications/qml/classification/data/Images_2D/test_data/Positive/Positive.zip b/src/modules/applications/qml/classification/data/Images_2D/test_data/Positive/Positive.zip new file mode 100644 index 000000000..11ddbf342 --- /dev/null +++ b/src/modules/applications/qml/classification/data/Images_2D/test_data/Positive/Positive.zip @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:6c42488cbedaac632ee9410108ba282a2765e9d057f34667afe32d4fefff8769 +size 136459955 diff --git a/src/modules/applications/qml/classification/data/__init__.py b/src/modules/applications/qml/classification/data/__init__.py new file mode 100644 index 000000000..23e771412 --- /dev/null +++ b/src/modules/applications/qml/classification/data/__init__.py @@ -0,0 +1,15 @@ +# Copyright 2022 The QUARK Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +"""Module containing all classification datasets""" diff --git a/src/modules/applications/qml/classification/data/data_handler/__init__.py b/src/modules/applications/qml/classification/data/data_handler/__init__.py new file mode 100644 index 000000000..e0539e5ef --- /dev/null +++ b/src/modules/applications/qml/classification/data/data_handler/__init__.py @@ -0,0 +1,15 @@ +# Copyright 2022 The QUARK Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +"""Package containing the dataset modules""" diff --git a/src/modules/applications/qml/classification/data/data_handler/data_handler_classification.py b/src/modules/applications/qml/classification/data/data_handler/data_handler_classification.py new file mode 100644 index 000000000..5ff9dfa6a --- /dev/null +++ b/src/modules/applications/qml/classification/data/data_handler/data_handler_classification.py @@ -0,0 +1,127 @@ +# Copyright 2021 The QUARK Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +from abc import ABC, abstractmethod + +import pandas as pd +from modules.core import * +from tensorboard.backend.event_processing.event_accumulator import EventAccumulator +from utils import end_time_measurement, start_time_measurement + + +class DataHandler(Core, ABC): + """ + The task of the DataHandler module is to translate the application’s data and problem specification into + preprocessed format. + """ + + def __init__(self, name): + """ + Constructor method. + """ + super().__init__() + self.dataset_name = name + self.classification_mark = None + + @staticmethod + def get_requirements() -> list[dict]: + """ + Returns requirements of this module. + + :return: List of dicts with requirements of this module + """ + return [ + {"name": "pandas", "version": "2.2.3"}, + {"name": "tensorboard", "version": "2.18.0"}, + ] + + def preprocess(self, input_data: dict, config: dict, **kwargs) -> tuple[dict, float]: + """ + In this module, the preprocessing step is transforming the data to the correct target format. + + :param input_data: collected information of the benchmarking process + :param config: config specifying the parameters of the training + :param kwargs: optional additional settings + :return: tuple with transformed problem and the time it took to map it + """ + start = start_time_measurement() + output = self.data_load(input_data, config) + + if "classification_metrics" in list(output.keys()): + self.classification_mark = True + + return output, end_time_measurement(start) + + def postprocess(self, input_data: dict, config: dict, **kwargs) -> tuple[dict, float]: + """ + In this module, the postprocessing step is transforming the data to the correct target format. + + :param input_data: Collected information of the benchmarking process + :param config: Config specifying the parameters of the training + :param kwargs: Optional additional settings + :return: Tuple with an output_dictionary and the time it took + """ + start = start_time_measurement() + return input_data, end_time_measurement(start) + + @abstractmethod + def data_load(self, gen_mod: dict, config: dict) -> dict: + """ + Helps to ensure that the model can effectively learn the underlying patterns and structure of the data, and + produce high-quality outputs. + + :param gen_mod: dictionary with collected information of the previous modules + :param config: config specifying the parameters of the data handler + :return: mapped problem and the time it took to create the mapping + """ + raise NotImplementedError + + # def generalisation(self) -> tuple[dict, float]: + # """ + # Compute generalisation metrics. + # :return: Evaluation and the time it took to create it + # """ + # metrics = {} + # time_taken = 0.0 + # return metrics, time_taken + + # @abstractmethod + # def evaluate(self, solution: dict) -> tuple[dict, float]: + # """ + # Computes the best loss values. + # + # :param solution: Dictionary containing the solution data + # :return: Evaluated solution and the time it took to evaluate it + # """ + # raise NotImplementedError + + @staticmethod + def tb_to_pd(logdir: str, rep: str) -> None: + """ + Converts TensorBoard event files in the specified log directory into a pandas DataFrame and saves it as a pickle + file. + + :param logdir: Path to the log directory containing TensorBoard event files + :param rep: Repetition number for naming the output file + """ + event_acc = EventAccumulator(logdir) + event_acc.Reload() + tags = event_acc.Tags() + tag_data = {} + for tag in tags["scalars"]: + data = event_acc.Scalars(tag) + tag_values = [d.value for d in data] + tag_data[tag] = tag_values + data = pd.DataFrame(tag_data, index=[d.step for d in data]) + data.to_pickle(f"{logdir}/data_{rep}.pkl") + data.to_pickle(f"{logdir}/data_{rep}.pkl") diff --git a/src/modules/applications/qml/classification/data/data_handler/image_data.py b/src/modules/applications/qml/classification/data/data_handler/image_data.py new file mode 100644 index 000000000..bac76b79f --- /dev/null +++ b/src/modules/applications/qml/classification/data/data_handler/image_data.py @@ -0,0 +1,508 @@ +# Copyright 2021 The QUARK Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import logging +import os +from typing import Iterable, TypedDict + +import numpy as np +import pandas as pd +import pkg_resources +import torch +import torch.nn as nn +import torchvision +from modules.applications.qml.classification.data.data_handler.data_handler_classification import DataHandler +from modules.applications.qml.classification.data.data_handler.metrics_classification import MetricsClassification +from modules.applications.qml.classification.training.hybrid import Hybrid +from PIL import Image +from torch.utils.data import DataLoader, Dataset +from torchvision import models, transforms +from tqdm import tqdm +from utils import end_time_measurement, start_time_measurement +import zipfile + + +class ImageData(DataHandler): + """ + A data handler for image datasets. This class loads a dataset from a specified path and provides + methods for data transformation and evaluation. + + """ + + def __init__(self): + """ + The continuous data class loads a dataset from the path + src/modules/applications/QML/classification/data + """ + super().__init__("") + self.submodule_options = ["Hybrid"] + self.transformation = None + self.dataset = None + self.n_registers = None + self.gc = None + self.n_qubits = None + self.data_folder = pkg_resources.resource_filename("modules.applications.qml.classification.data", "Images_2D") + + @staticmethod + def get_requirements() -> list[dict]: + """ + Returns requirements of this module + + :return: list of dict with requirements of this module + :rtype: list[dict] + """ + return [ + {"name": "pandas", "version": "2.2.3"}, + {"name": "pillow", "version": "11.1.0"}, + {"name": "torch", "version": "2.2.2"}, + {"name": "torchvision", "version": "0.17.2"}, + {"name": "tqdm", "version": "4.67.1"}, + {"name": "numpy", "version": "1.26.4"}, + {"name": "scikit-learn", "version": "1.4.2"}, # Used in MetricsClassification + {"name": "qiskit_aer", "version": "0.15.1"}, # Used in MetricsClassification/qleet + {"name": "tensorboard", "version": "2.18.0"}, # Used in DataHandler + ] + + def get_default_submodule(self, option: str) -> Hybrid: + """ + Returns the default submodule based on the given option. + + :param option: Option name + :raises NotImplementedError: If called, since this module has no submodules + """ + if option == "Hybrid": + return Hybrid() + else: + raise NotImplementedError(f"Transformation Option {option} not implemented") + + def get_parameter_options(self) -> dict: + """ + Returns the configurable settings for this application + + :return: + + .. code-block:: python + + return { + "data_set": { + "values": ["Concrete_Crack", "mnist"], + "description": "Which dataset do you want to use?", + }, + "n_images_per_class": { + "values": [100, 1000, 2000, 3000], + "description": "Number of images to extract for each class", + }, + "n_classes": { + "values": [2, 4, 10], + "description": "How many classes to benchmark on (Concrete_Crack only works with 2)?", + }, + "noise_sigma": { + "values": [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0], + "description": "Variance of gaussian noise", + }, + } + + """ + return { + "data_set": { + "values": ["Concrete_Crack", "mnist"], + "description": "Which dataset do you want to use?", + }, + "n_images_per_class": { + "values": [100, 1000, 2000, 3000], + "description": "Number of images to extract for each class", + }, + "n_classes": { + "values": [2, 4, 10], + "description": "How many classes to benchmark on (Concrete_Crack only works with 2)?", + }, + "noise_sigma": { + "values": [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0], + "description": "Variance of gaussian noise", + }, + } + + class Config(TypedDict): + """ + Attributes of a valid config + + .. code-block:: python + + data_set: str + n_images_per_class: int + n_classes: int + noise_sigma: float + """ + + data_set: str + n_images_per_class: int + n_classes: int + noise_sigma: float + + class CustomDataset(Dataset): + """ + Dataset object used by Pytorch. + """ + + def __init__(self, embeddings_file: str, index_selection: Iterable[int], transform: any = None, + target_transform: any = None): + """ + Constructor method. + :param embeddings_file: Path to the CSV file containing the embeddings + :param index_selection: Iterable with indeces + :param transform: Transformation applied to each image + :param target_transform: Target transform applied to each image + """ + self.embeddings_df = pd.read_csv(embeddings_file, index_col=0).iloc[index_selection] + self.transform = transform + self.target_transform = target_transform + + def __len__(self) -> int: + """ + Returns length of embedding array. + """ + return len(self.embeddings_df) + + def __getitem__(self, idx: str) -> tuple[any, int, str]: + """ + Returns data array, label, and path. + """ + data = self.embeddings_df.iloc[idx, 2:].values.astype(np.float32) + label = self.embeddings_df.iloc[idx, 0] + path = self.embeddings_df.iloc[idx, 1] + return data, self.map_label(label), path + + def map_label(self, label: str): + """ + Converts string labels to integers. + + :param label: String label. Can either be "Positive" or "Negative" + :return: Integer number corresponding to the label + """ + if label == "Negative": + return 0 + elif label == "Positive": + return 1 + else: + raise ValueError("Invalid label") + + def data_load(self, gen_mod: dict, config: dict) -> dict: + """ + Loads chosen dataset and splits into training and validation sets. + + :param gen_mod: Dictionary with collected information of the previous modules + :param config: Config specifying the parameters of the data handler + :return: Returns the mapped problem and the time it took to create the mapping + """ + self.dataset_name = config["data_set"] + self.n_qubits = gen_mod["n_qubits"] + self.n_classes = config["n_classes"] + self.n_images_per_class = config["n_images_per_class"] + total_n_images = config["n_classes"] * config["n_images_per_class"] + noise_sigma = config["noise_sigma"] + + if self.dataset_name == "Concrete_Crack": + if config["n_classes"] != 2: + raise Exception("Sorry, number of classes does not work for this dataset. Should be 2.") + + logging.info("Creating index") + self.create_data_index() + logging.info("Embedding dataset") + embeddings_file = self.embed_dataset(n_images_per_class=self.n_images_per_class, noise_sigma=noise_sigma) + self.dataset_train, self.dataset_val, self.dataset_test = self.create_torch_dataset( + total_n_images, embeddings_file + ) + + elif self.dataset_name == "mnist": + self.dataset_train, self.dataset_val, self.dataset_test = self.create_mnist_dataset( + self.n_classes, self.n_images_per_class + ) + + else: + logging.error("Unknown use case") + + application_config = { + "dataset_name": self.dataset_name, + "n_qubits": self.n_qubits, + "n_classes": self.n_classes, + "dataset_train": self.dataset_train, + "dataset_val": self.dataset_val, + "dataset_test": self.dataset_test, + "store_dir_iter": gen_mod["store_dir_iter"], + } + + self.classification_metrics = MetricsClassification() + application_config["classification_metrics"] = self.classification_metrics + + return application_config + + def create_mnist_dataset(self, n_classes: int, n_images_per_class: int): + transform = transforms.Compose( + [ + transforms.ToTensor(), + transforms.Normalize((0.5,), (0.5,)), + transforms.Lambda(lambda x: x.repeat(3, 1, 1)), + ] + ) + """ + Creates a dataset for mnist with the specified number of classes and images per class. + + :param n_classes: Number of classes to include in the classification + :param n_images_per_class: Number of images to include for each class + :return: Tuple of DataLoader objects for training, validation, and testing + """ + # TODO this method is not creating image embeddings. + # The dataloading for the concrete crack images does: + # 1. Indexing and sampling of the files + # 2. Embedding of the sampled images using resnet + # 3. Packaging into a torch dataloader + # The dataloading for mnist instead does only steps 1 and 3. + # Step 2 for mnist is done in the training part (Hybrid module). + # This lack of consistency should be fixed in the future. + batch_size = 64 + + trainset = torchvision.datasets.MNIST(root=self.data_folder, train=True, download=True, transform=transform) + + idx = self.keep_first_k_ones(torch.as_tensor(trainset.targets) == 0, k=n_images_per_class) + for j in range(1, n_classes): + idx += self.keep_first_k_ones(torch.as_tensor(trainset.targets) == j, k=n_images_per_class) + subset_train = torch.utils.data.dataset.Subset(trainset, np.where(idx == 1)[0]) + + trainloader = torch.utils.data.DataLoader(subset_train, batch_size=batch_size, shuffle=True, num_workers=0) + + testset = torchvision.datasets.MNIST(root=self.data_folder, train=False, download=True, transform=transform) + + idx = self.keep_first_k_ones(torch.as_tensor(testset.targets) == 0, k=n_images_per_class) + for j in range(1, n_classes): + idx += self.keep_first_k_ones(torch.as_tensor(testset.targets) == j, k=n_images_per_class) + subset_test = torch.utils.data.dataset.Subset(testset, np.where(idx == 1)[0]) + testloader = torch.utils.data.DataLoader(subset_test, batch_size=batch_size, shuffle=False, num_workers=0) + + return trainloader, testloader, testloader + + @staticmethod + def keep_first_k_ones(tensor: torch.Tensor, k: int = 1000) -> torch.Tensor: + """ + Keeps only the first k ones in a 1-D tensor of 0s and 1s and sets the rest to 0. + + :param tensor: A 1-D tensor containing 0s and 1s + :param k: The number of ones to keep. Defaults to 1000 + :return: A new tensor with only the first k ones remaining + """ + + indices = (tensor == 1).nonzero(as_tuple=False).flatten() # indices of all 1s + if len(indices) > k: + indices_to_zero = indices[k:] # indices of ones to change to zeros + new_tensor = tensor.clone() # creating a clone is crucial to avoid modifying the input tensor + new_tensor[indices_to_zero] = 0 + return new_tensor + else: + return tensor.clone() + + def create_torch_dataset(self, tot_train_test_datapoints: int, embeddings_file: str)\ + -> tuple[DataLoader, DataLoader, DataLoader]: + """ + Creates training, validation, and test dataloaders. + + :param tot_train_test_datapoints: Total number of data points to be used for training and testing + :param embeddings_file: Path to the CSV file containing the embeddings + :return: Tuple of DataLoader objects for training, validation, and testing + """ + train_indexes = [] + val_indexes = [] + test_indexes = [] + p_val = 0.15 + p_test = 0.10 + + np.random.seed(42) + for j in range(tot_train_test_datapoints): + p = np.random.rand() + if p < p_val: + val_indexes.append(j) + elif p < p_val + p_test: + test_indexes.append(j) + else: + train_indexes.append(j) + + train_dataset = self.CustomDataset(embeddings_file=embeddings_file, index_selection=train_indexes) + val_dataset = self.CustomDataset(embeddings_file=embeddings_file, index_selection=val_indexes) + test_dataset = self.CustomDataset(embeddings_file=embeddings_file, index_selection=test_indexes) + + train_dataloader = DataLoader(train_dataset, batch_size=32, shuffle=True) + val_dataloader = DataLoader(val_dataset, batch_size=32, shuffle=True) + test_dataloader = DataLoader(test_dataset, batch_size=32, shuffle=True) + + return train_dataloader, val_dataloader, test_dataloader + + def create_data_index(self, overwrite: bool = True) -> None: + """ + Crawls the filenames in the data folder and writes an index in a CSV file. + + :param overwrite: Set to True if file needs to be overwritten + """ + output_path = os.path.join(self.data_folder, "test_data", "index.csv") + if os.path.exists(output_path) and not overwrite: + raise FileExistsError( + "Index file already exists. To overwrite, call the function with the argument overwrite=True" + ) + + index_list = [] + for root, dirs, files in os.walk(os.path.join(self.data_folder, "test_data")): + for file in files: + if file.endswith(".zip") and len(files) == 1: + # Unzip files if still unzipped + logging.info("Unzipping files from " + os.path.join(root, file) + ". This may take a while.") + with zipfile.ZipFile(os.path.join(root, file), "r") as zip_ref: + zip_ref.extractall(os.path.join(root)) + for root, dirs, files in os.walk(os.path.join(self.data_folder, "test_data")): + for file in files: + if file.endswith(".jpg"): + label = os.path.basename(root) + index_list.append( + { + "file": f"{label.lower()[0]}_{file}", + "label": label, + "path": os.path.join(root, file), + } + ) + + index_df = pd.DataFrame(index_list) + index_df = index_df.set_index("file") + index_df.to_csv(output_path) + + def sample_index(self, n_samples_per_class: int, random_state: int = 42) -> pd.DataFrame: + """ + Samples images from the index file. + + :param n_samples_per_class: Number of samples to extract for each class + :param random_state: Random seed for reproducibility + :return: A pandas DataFrame containing the sampled images + """ + index_df = pd.read_csv(os.path.join(self.data_folder, "test_data", "index.csv"), index_col=0) + min_images_per_class = min(index_df.label.value_counts()) + assert n_samples_per_class <= min_images_per_class, ( + f"n_samples_per_class is too big. Only {min_images_per_class} images per label are available." + ) + + shuffled_df = index_df.sample(frac=1.0, random_state=random_state) + negative_samples_df = shuffled_df[shuffled_df.label == "Negative"].iloc[0:n_samples_per_class] + positive_samples_df = shuffled_df[shuffled_df.label == "Positive"].iloc[0:n_samples_per_class] + + return pd.concat([negative_samples_df, positive_samples_df]) + + def add_noise(self, image: np.array, sigma: float, mean: float = 0.0) -> np.array: + """ + Adds Gaussian noise to the input images with sigma. + + :param image: Image to add noise to + :param sigma: Standard deviation of the Gaussian noise + :param mean: Mean of the Gaussian noise + :return: Numpy array with pixel values between 0 and 1 + """ + gaussian = np.random.normal(mean, sigma, image.shape) + noisy_df = image + gaussian + noisy_df = np.clip(noisy_df, 0, 1) + return noisy_df + + def embed_dataset(self, n_images_per_class: int, noise_sigma: float) -> pd.DataFrame: + """ + Embed images from the data folder using the pretrained Resnet18 model. Stores the embeddings in a CSV file. + Embeddings are added in an incremental way if an embedding CSV file already exists. + + :param n_images_per_class: Number of images to embed for each class + :param noise_sigma: Sigma of Gaussian noise applied to the dataset + :return: Pandas DataFrame with embed data + """ + resnet18_embedder = self.build_truncated_resnet_model() + resnet18_embedder.eval() + + normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]) + + embedding_file = os.path.join(self.data_folder, "embeddings.csv") + existing_embedding_df = pd.DataFrame( + columns=["file", "label", "path"] + [f"e_{j:03d}" for j in range(512)] + ).set_index("file") + + sampled_df = self.sample_index(n_samples_per_class=n_images_per_class) + output_df = pd.merge( + left=sampled_df, + right=existing_embedding_df, + how="left", + on=["file", "label", "path"], + ) + index_to_embed = sampled_df.index.drop(existing_embedding_df.index) + + imgs_array = np.empty((len(index_to_embed), 3, 227, 227)) + counter = 0 + for idx, row in tqdm( + sampled_df.loc[index_to_embed].iterrows(), + total=sampled_df.loc[index_to_embed].shape[0], + ): + img_path = row["path"] + img_array = self.load_image(img_path) + if noise_sigma > 0: + img_array = self.add_noise(img_array, noise_sigma) + img_array = np.rollaxis(img_array, -1, 0) + imgs_array[counter] = img_array + counter += 1 + + embeddings = [] + batch_size = 16 + for batch_start in tqdm(range(0, len(imgs_array), batch_size)): + imgs_tensor = torch.tensor(imgs_array[batch_start: batch_start + batch_size]).float() + imgs_tensor = normalize(imgs_tensor) + pred = resnet18_embedder(imgs_tensor) + embeddings.extend(pred.detach().reshape(-1, 512).tolist()) + + output_df.loc[index_to_embed, [f"e_{j:03d}" for j in range(512)]] = embeddings + output_df.to_csv(embedding_file) + + return embedding_file + + def build_truncated_resnet_model(self) -> nn.Module: + """ + Remove the last layer from a pretrained Resnet18 model. + + :return: Truncated torch model + """ + # resnet18 = models.resnet18(pretrained=True) # Deprecated + resnet18 = models.resnet18(weights=models.ResNet18_Weights.DEFAULT) + keep_layers = list(resnet18.children())[:-1] + truncated_resnet18 = nn.Sequential(*keep_layers) + return truncated_resnet18 + + def load_image(self, image_path: str) -> np.array: + """ + Load an image from disk. + + :param image_path: System path to the loadable image + :return: Numpy array with pixel values between 0 and 1 + """ + img = Image.open(image_path) + img_array = np.array(img) / 255 + return img_array + + def evaluate(self, solution: list, **kwargs) -> tuple[float, float]: + """ + Calculate Accuracy in the original data set. + + :param param1: + :type param1: list + :return: Accuracy for the test case and the time it took to calculate it. + :rtype: tuple(float, float) + """ + start = start_time_measurement() + evaluate_dict = None + + return evaluate_dict, end_time_measurement(start) diff --git a/src/modules/applications/qml/classification/data/data_handler/metrics_classification.py b/src/modules/applications/qml/classification/data/data_handler/metrics_classification.py new file mode 100644 index 000000000..2f4edbea2 --- /dev/null +++ b/src/modules/applications/qml/classification/data/data_handler/metrics_classification.py @@ -0,0 +1,92 @@ +# Copyright 2021 The QUARK Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + + +import numpy as np +import sklearn.metrics + + +class MetricsClassification: + """ + A class to compute classification metrics for generated samples + """ + + def __init__(self) -> None: + """ + Constructor method. + """ + self.submodule_options = [] + pass + + def get_metrics(self, y_pred: np.array, y_true: np.array) -> dict[str, float]: + """ + Method that determines all classification metrics. + + :param y_pred: Predicted labels + :param y_true: Real labels + :return: Dictionary with classification metrics + """ + + results = { + "accuracy": self.accuracy(y_pred, y_true), + "recall": self.recall(y_pred, y_true), + "precision": self.precision(y_pred, y_true), + "f1_score": self.f1_score(y_pred, y_true), + } + + return results + + def accuracy(self, y_pred: np.array, y_true: np.array) -> float: + """ + Method to determine the accuracy. + + :param y_pred: Predicted labels + :param y_true: Real labels + :return: Accuracy + """ + accuracy = sklearn.metrics.accuracy_score(y_true, y_pred) + return accuracy + + def recall(self, y_pred: np.array, y_true: np.array) -> float: + """ + Method to determine the recall. + + :param y_pred: Predicted labels + :param y_true: Real labels + :return: Recall + """ + recall = sklearn.metrics.recall_score(y_true, y_pred, zero_division=1.0, average="macro") + return recall + + def precision(self, y_pred: np.array, y_true: np.array) -> float: + """ + Method to determine the precision. + + :param y_pred: Predicted labels + :param y_true: Real labels + :return: Precision + """ + precision = sklearn.metrics.precision_score(y_true, y_pred, zero_division=1.0, average="macro") + return precision + + def f1_score(self, y_pred: np.array, y_true: np.array) -> float: + """ + Method to determine the F1 score. + + :param y_pred: Predicted labels + :param y_true: Real labels + :return: F1_score + """ + f1_score = sklearn.metrics.f1_score(y_true, y_pred, zero_division=1.0, average="macro") + return f1_score diff --git a/src/modules/applications/qml/classification/quantum_model.py b/src/modules/applications/qml/classification/quantum_model.py new file mode 100644 index 000000000..c912dbe7a --- /dev/null +++ b/src/modules/applications/qml/classification/quantum_model.py @@ -0,0 +1,278 @@ +from typing import Optional + +import numpy as np +import pennylane as qml +import torch +import torch.nn as nn +from qiskit import QuantumCircuit +from qiskit.circuit import Parameter +from qiskit.quantum_info import SparsePauliOp +from qiskit_machine_learning.connectors import TorchConnector +from qiskit_machine_learning.neural_networks import EstimatorQNN +from torchvision import models + + +class EarlyStopping(object): + """ + Stops training when a monitored quantity has stopped improving. + """ + + def __init__(self, mode: str = "min", min_delta: float = 0, patience: int = 10, percentage: bool = False): + """ + Constructor method. + + :param mode: Can be "min" or "max". With "min" training stops when quantity monitored has stopped decreasing + :param min_delta: Minimum change in the monitored quantity to qualify as an improvement + :param patience: Number of epochs with no improvement after which training will be stopped + :param percentage: If True, the min_delta is interpreted as a percentage of the best score + """ + self.mode = mode + self.min_delta = min_delta + self.patience = patience + self.best = None + self.num_bad_epochs = 0 + self.is_better = None + self._init_is_better(mode, min_delta, percentage) + + if patience == 0: + self.is_better = lambda a, b: True + self.step = lambda a: False + + def step(self, metrics: float) -> bool: + """ + Checks if the training should be stopped. + + :param metrics: Value of the monitored metric + :return: True if training should be stopped, False otherwise + """ + if self.best is None: + self.best = metrics + return False + + if torch.isnan(metrics): + return True + + if self.is_better(metrics, self.best): + self.num_bad_epochs = 0 + self.best = metrics + else: + self.num_bad_epochs += 1 + + if self.num_bad_epochs >= self.patience: + return True + + return False + + def _init_is_better(self, mode: str, min_delta: float, percentage: bool) -> None: + """ + Check if initial metric value is better. + + :param mode: Can be "min" or "max" + :param min_delta: Minimum change in the monitored quantity to qualify as an improvement + :param percentage: If True, the min_delta is interpreted as a percentage of the best score + """ + if mode not in {"min", "max"}: + raise ValueError("mode " + mode + " is unknown!") + if not percentage: + if mode == "min": + self.is_better = lambda a, best: a < best - min_delta + if mode == "max": + self.is_better = lambda a, best: a > best + min_delta + else: + if mode == "min": + self.is_better = lambda a, best: a < best - (best * min_delta / 100) + if mode == "max": + self.is_better = lambda a, best: a > best + (best * min_delta / 100) + + +class QuantumLayer(nn.Module): + """ + Torch module implementing a quantum layer. + """ + + def __init__(self, nqubits: int = 4, circuit_depth: int = 4, quantum_device: Optional[qml.Device] = None) -> None: + """ + Constructor method defining the quantum circuit. + + :param nqubits: Number of qubits in the quantum circuit + :param circuit_depth: Depth of the quantum circuit + :param quantum_device: The quantum device to run the circuit on. If None, uses default.qubit + """ + super(QuantumLayer, self).__init__() + self.n_qubits = nqubits + if quantum_device is None: + self.quantum_device = qml.device("default.qubit", wires=self.n_qubits) + else: + self.quantum_device = quantum_device + self.num_variational_layers = circuit_depth + self.model = self.build_circuit() + + def H_layer(self, nqubits: int) -> None: + """ + Layer of single-qubit Hadamard gates. + + :param nqubits: Number of qubits in the circuit + """ + for idx in range(nqubits): + self.circuit.h(idx) + + def RY_layer(self, w: list) -> None: + """ + Layer of parametrized qubit rotations around the y-axis. + + :param w: List of rotation angles for each qubit + """ + for idx, element in enumerate(w): + self.circuit.ry(element, idx) + + def entangling_layer(self, nqubits: int) -> None: + """ + Layer of CNOT-gates followed by another shifted layer of CNOT-gates. + + :param nqubits: Number of qubits in the circuit + """ + for i in range(0, nqubits - 1, 2): + self.circuit.cx(i, i + 1) + for i in range(1, nqubits - 1, 2): + self.circuit.cx(i, i + 1) + + def generate_pauliz_observables(self, nqubits: int) -> list: + """ + Pauli_Z-gates for each qubit of the circuit. + + :param nqubits: Number of qubits in the circuit + :return: List of SparsePauliOp objects representing the observables + """ + observables = [] + for i in range(nqubits): + pauli_string = ["I"] * nqubits + pauli_string[i] = "Z" + observables.append(SparsePauliOp.from_list([("".join(pauli_string), 1.0)])) + return observables + + def build_circuit(self) -> TorchConnector: + """ + Define the quantum node, i.e. variational quantum circuit plus device on which the circuit is executed. + :return: The quantum node (QNode) with the defined circuit and parameters + """ + + self.circuit = QuantumCircuit(self.n_qubits) + + input_params = [Parameter(f"input_{i}") for i in range(self.n_qubits)] + var_params = [Parameter(f"var_{i}") for i in range(self.num_variational_layers * self.n_qubits)] + self.params = input_params + var_params + + self.H_layer(self.n_qubits) + self.RY_layer(input_params) + + for i in range(self.num_variational_layers): + self.entangling_layer(self.n_qubits) + for j in range(self.n_qubits): + self.circuit.ry(var_params[i * self.n_qubits + j], j) + + observables = self.generate_pauliz_observables(self.n_qubits) + + self.qnn = EstimatorQNN( + circuit=self.circuit, + observables=observables, + input_params=input_params, + weight_params=var_params, + input_gradients=True, + ) + + model = TorchConnector(self.qnn) + return model + + def forward(self, x: torch.Tensor) -> torch.Tensor: + """ + Forward pass of the quantum layer. + """ + x = torch.tanh(x) * np.pi / 2.0 + return self.model(x) + + def draw(self) -> None: + """ + Print the quantum layer/circuit using the qiskit built-in functionality. + """ + print(self.circuit.draw(output="text")) + + +class QuantumModel(nn.Module): + """ + A hybrid quantum-classical neural network model for image classification. + + This model combines classical pre-processing layers (potentially including feature extraction like ResNet) with a + quantum layer for classification. It supports different datasets like 'Concrete_Crack' and 'mnist'. + """ + + def __init__(self, quantum_device: qml.Device = None, n_reduced_features: int = 4, circuit_depth: int = 1, + dataset_name: str = "Concrete_Crack", n_classes: int = 2): + """ + Initializes the QuantumModel. + + :param quantum_device: The PennyLane quantum device to run the quantum circuit on. + :param n_reduced_features: The number of features to reduce the input to before feeding into the quantum layer. + :param circuit_depth: The depth of the quantum circuit (number of layers in the QuantumLayer). + :param dataset_name: The name of the dataset. + :param n_classes: The number of classes to classify. + """ + super(QuantumModel, self).__init__() + self.submodule_options = [] + self.dataset_name = dataset_name + self.n_classes = n_classes + + if quantum_device is None: + quantum_device = qml.device("default.qubit", wires=n_reduced_features) + + self.quantum_layer = QuantumLayer( + nqubits=n_reduced_features, + quantum_device=quantum_device, + circuit_depth=circuit_depth, + ) + + if self.dataset_name == "Concrete_Crack": + self.image_features = 512 + self.model = nn.Sequential( + nn.Linear(self.image_features, n_reduced_features), + self.quantum_layer, + nn.Linear(n_reduced_features, self.n_classes), + ) + + # TODO the classical embeddings with resnet for mnist + # should be moved to the ImageData logic as it is done for the Concrete Crack use case. + elif self.dataset_name == "mnist": + self.image_features = 28 + # self.resnet18 = models.resnet18(pretrained=True) # Deprecated + self.resnet18 = models.resnet18(weights=models.ResNet18_Weights.DEFAULT) + self.fc_inputs = self.resnet18.fc.in_features + self.resnet18.fc = nn.Identity() + + self.model = nn.Sequential( + nn.Linear(self.fc_inputs, n_reduced_features), + self.quantum_layer, + nn.Linear(n_reduced_features, self.n_classes), + ) + else: + raise Exception(f"No valid dataset name {self.dataset_name}.") + + def get_quantum_circuit(self) -> tuple[qml.QNode, torch.nn.Parameter]: + """ + Retrieves the underlying quantum circuit and its parameters. + + :return: A tuple containing the quantum circuit (QNode) and the trainable parameters of the quantum circuit. + """ + return self.quantum_layer.circuit, self.quantum_layer.params + + def forward(self, x: torch.Tensor) -> torch.Tensor: + """ + Defines the forward pass of the model. + + :param x: The input tensor. For 'Concrete_Crack', this is expected to be pre-extracted features. For 'mnist', + this is expected to be the raw image tensor. + :return: The output tensor containing the model's predictions (logits). + """ + if self.dataset_name == "mnist": + with torch.no_grad(): + x = self.resnet18(x) + + return self.model(x) diff --git a/src/modules/applications/qml/classification/training/hybrid.py b/src/modules/applications/qml/classification/training/hybrid.py new file mode 100644 index 000000000..ee81957bd --- /dev/null +++ b/src/modules/applications/qml/classification/training/hybrid.py @@ -0,0 +1,374 @@ +# Copyright 2021 The QUARK Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from utils_mpi import get_comm, is_running_mpi +from utils import end_time_measurement, start_time_measurement +from tqdm import tqdm +from torch.optim.lr_scheduler import StepLR +from tensorboardX import SummaryWriter +from modules.core import Core +from modules.applications.qml.training import Training +from modules.applications.qml.metrics_quantum import MetricsQuantum +from modules.applications.qml.classification.quantum_model import QuantumModel +from matplotlib import axes, figure +import torch.nn as nn +import torch +from matplotlib import pyplot as plt +import logging +import time +from typing import TypedDict + +import matplotlib +matplotlib.use("Agg") # Use a non-interactive backend for matplotlib + + +try: + import cupy as np + + GPU = True + logging.info("Using CuPy, data processing on GPU") +except ModuleNotFoundError: + import numpy as np + + GPU = False + logging.info("CuPy not available, using vanilla numpy, data processing on CPU") + +MPI = is_running_mpi() +comm = get_comm() + + +class Hybrid(Core, Training): + def __init__(self): + """ + Constructor method. + """ + super().__init__() # "Hybrid") + + self.n_states_range: list + self.target: np.array + self.study_generalization: bool + self.generalization_metrics: dict + self.submodule_options = [] + self.writer: SummaryWriter + self.loss_func: callable + self.fig: figure + self.ax: axes.Axes + + @staticmethod + def get_requirements() -> list[dict]: + """ + Returns requirements of this module. + + :return: list of dict with requirements of this module + :rtype: list[dict] + """ + return [ + {"name": "matplotlib", "version": "3.9.3"}, + {"name": "torch", "version": "2.2.2"}, + {"name": "tqdm", "version": "4.67.1"}, + {"name": "numpy", "version": "1.26.4"}, + {"name": "tensorboardX", "version": "2.6.2.2"}, + ] + + def get_parameter_options(self) -> dict: + """ + Returns the configurable settings for the Quantum Circuit Born Machine. + + :return: + .. code-block:: python + + return { + "n_epochs": { + "values": [1, 3, 7, 10, 20], + "description": "How many epochs do you want to train?" + }, + "n_reduced_features": { + "values": [4], + "description": "Number of reduced features, also is the number of qubits in quantum layer" + }, + } + """ + return { + "n_epochs": { + "values": [1, 3, 7, 10, 20], + "description": "How many epochs do you want to train?", + }, + "n_reduced_features": { + "values": [4], + "description": "Number of reduced features, also is the number of qubits in quantum layer", + }, + } + + class Config(TypedDict): + """ + Attributes of a valid config. + + .. code-block:: python + + n_epochs: int + n_reduced_features: int + + """ + + n_epochs: int + n_reduced_features: int + + def get_default_submodule(self, option: str) -> None: + """ + Raises ValueError as this module has no submodules. + + :param option: Option name + :raises ValueError: If called, since this module has no submodules + """ + raise ValueError("This module has no submodules.") + + def postprocess(self, input_data: dict, config: dict, **kwargs) -> tuple[dict, float]: + """ + Here, the actual training of the machine learning model is done. + + :param input_data: Collected information of the benchmarking process + :param config: Training settings + :param kwargs: Optional additional arguments + :return: Training results and computation time of postprocessing + """ + start = start_time_measurement() + logging.info("Start training") + training_results = self.start_training(self.preprocessed_input, config, **kwargs) + + postprocessing_time = end_time_measurement(start) + logging.info(f"Training finished in {postprocessing_time / 1000} s.") + return training_results, postprocessing_time + + def setup_training(self, input_data: dict) -> None: + """ + Sets up the training configuration. + + :param input_data: Dictionary with the variables needed to start the training + """ + logging.info(f"Running config: [n_qubits={input_data['n_qubits']}]") + + self.study_classification = "classification_metrics" in list(input_data.keys()) + if self.study_classification: + self.classification_metrics = input_data["classification_metrics"] + + self.writer = SummaryWriter(input_data["store_dir_iter"]) + + def start_training(self, input_data: dict, config: Config, **kwargs: dict,) -> dict: + """ + This function starts the hybrid training. + + :param input_data: Dataset to be trained on + :param config: Config specifying the parameters of the training + :param kwargs: Optional additional settings + :return: Dictionary including the information of previous modules as well as of the training + """ + self.model = QuantumModel( + n_reduced_features=config["n_reduced_features"], + dataset_name=input_data["dataset_name"], + n_classes=input_data["n_classes"], + ) + quantum_metrics_class = MetricsQuantum() + circuit, params = self.model.get_quantum_circuit() + print("Quantum layer:\n", circuit.draw(output="text")) + quantum_metrics = quantum_metrics_class.get_metrics(circuit, params) + self.metrics.add_metric_batch( + { + "meyer-wallach": quantum_metrics["meyer-wallach"], + "expressibility_jsd": quantum_metrics["expressibility_jsd"], + } + ) + logging.info("Quantum metrics: %s", quantum_metrics) + + train_dataloader = input_data["dataset_train"] + val_dataloader = input_data["dataset_val"] + + loss_fn = nn.CrossEntropyLoss() + self.optimizer = torch.optim.Adam(self.model.parameters(), lr=0.001) + lr_scheduler_step = 100 + lr_scheduler_gamma = 0 + lr_scheduler = StepLR(self.optimizer, step_size=lr_scheduler_step, gamma=lr_scheduler_gamma) + + self.setup_training(input_data) + + size = None + input_data["MPI_size"] = size + input_data["store_dir_iter"] += f"_{input_data['dataset_name']}_qubits{input_data['n_qubits']}" + n_epochs = config["n_epochs"] + + self.n_states_range = range(2 ** input_data["n_qubits"]) + timing = self.Timing() + + for parameter in [ + "time_circuit", + "time_loss", + "train_accuracy", + "train_loss", + "validation_accuracy", + "validation_loss", + ]: + input_data[parameter] = [] + + best_loss = float("inf") + train_loss = float("inf") + time_circ = 0 + time_loss = 0 + self.fig, self.ax = plt.subplots() + + for epoch_idx in tqdm(range(n_epochs)): + y_trues = [] + y_preds = [] + train_losses = [] + + for val in tqdm(train_dataloader): + if len(val) == 3: + x, y_true, _ = val + elif len(val) == 2: + x, y_true = val + else: + raise ValueError("Unexpected data format") + timing.start_recording() + y_raw = self.model(x) + time_circ = timing.stop_recording() + y_logits = nn.Softmax(dim=1)(y_raw) + y_pred = np.argmax(y_logits.detach().numpy(), axis=1) + + timing.start_recording() + train_loss = loss_fn(y_raw, y_true) + train_losses.append(train_loss) + + y_trues = y_trues + list(y_true) + y_preds = y_preds + list(y_pred) + + time_loss = timing.stop_recording() + + self.optimizer.zero_grad() + train_loss.backward() + self.optimizer.step() + + lr_scheduler.step() + + if train_loss < best_loss: + best_loss = train_loss + + self.writer.add_scalar("loss/train", sum(train_losses) / len(train_losses), epoch_idx) + input_data["train_loss"].append(float(train_loss)) + + val_losses = [] + val_accuracies = [] + + for val in tqdm(val_dataloader): + if len(val) == 3: + x, y_true, _ = val + elif len(val) == 2: + x, y_true = val + else: + raise ValueError("Unexpected data format") + y_raw = self.model(x) + time_circ = timing.stop_recording() + y_logits = nn.Softmax(dim=1)(y_raw) + y_pred = np.argmax(y_logits.detach().numpy(), axis=1) + + val_losses.append(loss_fn(y_raw, y_true).detach().numpy()) + val_accuracies.append(sum(y_pred == y_true.detach().numpy()) / len(y_pred)) + + val_loss = np.mean(val_losses) + val_accuracy = np.mean(val_accuracies) + + self.writer.add_scalar("metrics/accuracy/validation", val_accuracy, epoch_idx) + self.writer.add_scalar("loss/validation", val_loss, epoch_idx) + + input_data["validation_loss"].append(float(val_loss)) + input_data["validation_accuracy"].append(float(val_accuracy)) + + metrics = self.classification_metrics.get_metrics(y_preds, y_trues) + train_accuracy = 0 + for metric_name, metric_value in metrics.items(): + self.writer.add_scalar(f"metrics/{metric_name}/train", metric_value, epoch_idx) + if metric_name == "accuracy": + train_accuracy = metric_value + else: + train_accuracy = metrics["accuracy"] + + input_data["train_accuracy"].append(float(train_accuracy)) + + logging.info( + f"[Epoch {epoch_idx}/{n_epochs}] " + f"[Train Loss: {train_loss:.5f}] " + f"[Train Accuracy: {train_accuracy:.5f}] " + f"[Validation Loss: {val_loss:.5f}] " + f"[Validation Accuracy: {val_accuracy:.5f}] " + f"[Circuit processing: {time_circ:.3f} ms] " + f"[Loss processing: {time_loss:.3f} ms]" + ) + + self.trained = True + + self.metrics.add_metric_batch({"val_accuracy": val_accuracy, "train_accuracy": train_accuracy}) + + plt.close() + self.writer.flush() + self.writer.close() + + return input_data + + class Timing: + """ + This module is an abstraction of time measurement for both CPU and GPU processes. + """ + + def __init__(self): + """ + Constructor method. + """ + + if GPU: + self.start_cpu: time.perf_counter + else: + self.start_gpu: np.cuda.Event + self.end_gpu: time.perf_counter + + self.start_recording = self.start_recording_gpu if GPU else self.start_recording_cpu + self.stop_recording = self.stop_recording_gpu if GPU else self.stop_recording_cpu + + def start_recording_cpu(self) -> None: + """ + This is a function to start time measurement on the CPU. + """ + self.start_cpu = start_time_measurement() + + def stop_recording_cpu(self) -> float: + """ + This is a function to stop time measurement on the CPU. + + .return: Elapsed time in milliseconds + """ + return end_time_measurement(self.start_cpu) + + def start_recording_gpu(self) -> None: + """ + This is a function to start time measurement on the GPU. + """ + self.start_gpu = np.cuda.Event() + self.end_gpu = np.cuda.Event() + self.start_gpu.record() + + def stop_recording_gpu(self) -> float: + """ + This is a function to stop time measurement on the GPU. + + :return: Elapsed time in milliseconds + """ + self.end_gpu.record() + self.end_gpu.synchronize() + return np.cuda.get_elapsed_time(self.start_gpu, self.end_gpu) diff --git a/src/modules/applications/qml/DataHandler.py b/src/modules/applications/qml/data_handler.py similarity index 100% rename from src/modules/applications/qml/DataHandler.py rename to src/modules/applications/qml/data_handler.py diff --git a/src/modules/applications/qml/generative_modeling/circuits/CircuitCardinality.py b/src/modules/applications/qml/generative_modeling/circuits/circuit_cardinality.py similarity index 90% rename from src/modules/applications/qml/generative_modeling/circuits/CircuitCardinality.py rename to src/modules/applications/qml/generative_modeling/circuits/circuit_cardinality.py index c9cb15f8f..3bc206748 100644 --- a/src/modules/applications/qml/generative_modeling/circuits/CircuitCardinality.py +++ b/src/modules/applications/qml/generative_modeling/circuits/circuit_cardinality.py @@ -14,11 +14,11 @@ from typing import TypedDict, Union -from modules.applications.qml.generative_modeling.circuits.CircuitGenerative import CircuitGenerative -from modules.applications.qml.generative_modeling.mappings.LibraryQiskit import LibraryQiskit -from modules.applications.qml.generative_modeling.mappings.LibraryPennylane import LibraryPennylane -from modules.applications.qml.generative_modeling.mappings.PresetQiskitNoisyBackend import PresetQiskitNoisyBackend -from modules.applications.qml.generative_modeling.mappings.CustomQiskitNoisyBackend import CustomQiskitNoisyBackend +from modules.applications.qml.generative_modeling.circuits.circuit_generative import CircuitGenerative +from modules.applications.qml.generative_modeling.mappings.library_qiskit import LibraryQiskit +from modules.applications.qml.generative_modeling.mappings.library_pennylane import LibraryPennylane +from modules.applications.qml.generative_modeling.mappings.preset_qiskit_noisy_backend import PresetQiskitNoisyBackend +from modules.applications.qml.generative_modeling.mappings.custom_qiskit_noisy_backend import CustomQiskitNoisyBackend class CircuitCardinality(CircuitGenerative): diff --git a/src/modules/applications/qml/generative_modeling/circuits/CircuitCopula.py b/src/modules/applications/qml/generative_modeling/circuits/circuit_copula.py similarity index 90% rename from src/modules/applications/qml/generative_modeling/circuits/CircuitCopula.py rename to src/modules/applications/qml/generative_modeling/circuits/circuit_copula.py index bcc759359..135c0ded1 100644 --- a/src/modules/applications/qml/generative_modeling/circuits/CircuitCopula.py +++ b/src/modules/applications/qml/generative_modeling/circuits/circuit_copula.py @@ -17,11 +17,11 @@ from scipy.special import binom -from modules.applications.qml.generative_modeling.circuits.CircuitGenerative import CircuitGenerative -from modules.applications.qml.generative_modeling.mappings.LibraryQiskit import LibraryQiskit -from modules.applications.qml.generative_modeling.mappings.LibraryPennylane import LibraryPennylane -from modules.applications.qml.generative_modeling.mappings.PresetQiskitNoisyBackend import PresetQiskitNoisyBackend -from modules.applications.qml.generative_modeling.mappings.CustomQiskitNoisyBackend import CustomQiskitNoisyBackend +from modules.applications.qml.generative_modeling.circuits.circuit_generative import CircuitGenerative +from modules.applications.qml.generative_modeling.mappings.library_qiskit import LibraryQiskit +from modules.applications.qml.generative_modeling.mappings.library_pennylane import LibraryPennylane +from modules.applications.qml.generative_modeling.mappings.preset_qiskit_noisy_backend import PresetQiskitNoisyBackend +from modules.applications.qml.generative_modeling.mappings.custom_qiskit_noisy_backend import CustomQiskitNoisyBackend class CircuitCopula(CircuitGenerative): diff --git a/src/modules/applications/qml/generative_modeling/circuits/CircuitGenerative.py b/src/modules/applications/qml/generative_modeling/circuits/circuit_generative.py similarity index 94% rename from src/modules/applications/qml/generative_modeling/circuits/CircuitGenerative.py rename to src/modules/applications/qml/generative_modeling/circuits/circuit_generative.py index 9704ab236..c28457b52 100644 --- a/src/modules/applications/qml/generative_modeling/circuits/CircuitGenerative.py +++ b/src/modules/applications/qml/generative_modeling/circuits/circuit_generative.py @@ -13,10 +13,10 @@ # limitations under the License. from abc import ABC +from modules.core import Core +from utils import start_time_measurement, end_time_measurement -from modules.applications.qml.Circuit import Circuit -from modules.Core import Core -from utils import end_time_measurement, start_time_measurement +from modules.applications.qml.circuit import Circuit class CircuitGenerative(Circuit, Core, ABC): diff --git a/src/modules/applications/qml/generative_modeling/circuits/CircuitStandard.py b/src/modules/applications/qml/generative_modeling/circuits/circuit_standard.py similarity index 89% rename from src/modules/applications/qml/generative_modeling/circuits/CircuitStandard.py rename to src/modules/applications/qml/generative_modeling/circuits/circuit_standard.py index 8bd11fd65..e32bc9166 100644 --- a/src/modules/applications/qml/generative_modeling/circuits/CircuitStandard.py +++ b/src/modules/applications/qml/generative_modeling/circuits/circuit_standard.py @@ -14,11 +14,11 @@ from typing import TypedDict, Union -from modules.applications.qml.generative_modeling.circuits.CircuitGenerative import CircuitGenerative -from modules.applications.qml.generative_modeling.mappings.LibraryQiskit import LibraryQiskit -from modules.applications.qml.generative_modeling.mappings.LibraryPennylane import LibraryPennylane -from modules.applications.qml.generative_modeling.mappings.PresetQiskitNoisyBackend import PresetQiskitNoisyBackend -from modules.applications.qml.generative_modeling.mappings.CustomQiskitNoisyBackend import CustomQiskitNoisyBackend +from modules.applications.qml.generative_modeling.circuits.circuit_generative import CircuitGenerative +from modules.applications.qml.generative_modeling.mappings.library_qiskit import LibraryQiskit +from modules.applications.qml.generative_modeling.mappings.library_pennylane import LibraryPennylane +from modules.applications.qml.generative_modeling.mappings.preset_qiskit_noisy_backend import PresetQiskitNoisyBackend +from modules.applications.qml.generative_modeling.mappings.custom_qiskit_noisy_backend import CustomQiskitNoisyBackend class CircuitStandard(CircuitGenerative): diff --git a/src/modules/applications/qml/generative_modeling/data/data_handler/ContinuousData.py b/src/modules/applications/qml/generative_modeling/data/data_handler/continuous_data.py similarity index 98% rename from src/modules/applications/qml/generative_modeling/data/data_handler/ContinuousData.py rename to src/modules/applications/qml/generative_modeling/data/data_handler/continuous_data.py index 3479135c7..1ea275e3d 100644 --- a/src/modules/applications/qml/generative_modeling/data/data_handler/ContinuousData.py +++ b/src/modules/applications/qml/generative_modeling/data/data_handler/continuous_data.py @@ -19,9 +19,9 @@ import pkg_resources from utils import start_time_measurement, end_time_measurement -from modules.applications.qml.generative_modeling.transformations.MinMax import MinMax -from modules.applications.qml.generative_modeling.transformations.PIT import PIT -from modules.applications.qml.generative_modeling.data.data_handler.DataHandlerGenerative import DataHandlerGenerative +from modules.applications.qml.generative_modeling.transformations.min_max import MinMax +from modules.applications.qml.generative_modeling.transformations.pit import PIT +from modules.applications.qml.generative_modeling.data.data_handler.data_handler_generative import DataHandlerGenerative class ContinuousData(DataHandlerGenerative): diff --git a/src/modules/applications/qml/generative_modeling/data/data_handler/DataHandlerGenerative.py b/src/modules/applications/qml/generative_modeling/data/data_handler/data_handler_generative.py similarity index 97% rename from src/modules/applications/qml/generative_modeling/data/data_handler/DataHandlerGenerative.py rename to src/modules/applications/qml/generative_modeling/data/data_handler/data_handler_generative.py index 81587a0f4..be55733cb 100644 --- a/src/modules/applications/qml/generative_modeling/data/data_handler/DataHandlerGenerative.py +++ b/src/modules/applications/qml/generative_modeling/data/data_handler/data_handler_generative.py @@ -12,16 +12,16 @@ # See the License for the specific language governing permissions and # limitations under the License. -import os import pickle +import os from abc import ABC +from qiskit import qpy import numpy as np -from qiskit import qpy -from modules.applications.qml.DataHandler import DataHandler -from modules.Core import Core -from utils import end_time_measurement, start_time_measurement +from modules.core import Core +from modules.applications.qml.data_handler import DataHandler +from utils import start_time_measurement, end_time_measurement class DataHandlerGenerative(Core, DataHandler, ABC): diff --git a/src/modules/applications/qml/generative_modeling/data/data_handler/DiscreteData.py b/src/modules/applications/qml/generative_modeling/data/data_handler/discrete_data.py similarity index 96% rename from src/modules/applications/qml/generative_modeling/data/data_handler/DiscreteData.py rename to src/modules/applications/qml/generative_modeling/data/data_handler/discrete_data.py index cf7fdebf1..3336003d7 100644 --- a/src/modules/applications/qml/generative_modeling/data/data_handler/DiscreteData.py +++ b/src/modules/applications/qml/generative_modeling/data/data_handler/discrete_data.py @@ -19,9 +19,9 @@ import numpy as np -from modules.applications.qml.generative_modeling.circuits.CircuitCardinality import CircuitCardinality -from modules.applications.qml.generative_modeling.data.data_handler.DataHandlerGenerative import DataHandlerGenerative -from modules.applications.qml.generative_modeling.metrics.MetricsGeneralization import MetricsGeneralization +from modules.applications.qml.generative_modeling.circuits.circuit_cardinality import CircuitCardinality +from modules.applications.qml.generative_modeling.data.data_handler.data_handler_generative import DataHandlerGenerative +from modules.applications.qml.generative_modeling.metrics.metrics_generalization import MetricsGeneralization from utils import start_time_measurement, end_time_measurement diff --git a/src/modules/applications/qml/generative_modeling/data/MG_2D.npy b/src/modules/applications/qml/generative_modeling/data/mg_2d.npy similarity index 100% rename from src/modules/applications/qml/generative_modeling/data/MG_2D.npy rename to src/modules/applications/qml/generative_modeling/data/mg_2d.npy diff --git a/src/modules/applications/qml/generative_modeling/data/O_2D.npy b/src/modules/applications/qml/generative_modeling/data/o_2d.npy similarity index 100% rename from src/modules/applications/qml/generative_modeling/data/O_2D.npy rename to src/modules/applications/qml/generative_modeling/data/o_2d.npy diff --git a/src/modules/applications/qml/generative_modeling/data/Stocks_2D.npy b/src/modules/applications/qml/generative_modeling/data/stocks_2d.npy similarity index 100% rename from src/modules/applications/qml/generative_modeling/data/Stocks_2D.npy rename to src/modules/applications/qml/generative_modeling/data/stocks_2d.npy diff --git a/src/modules/applications/qml/generative_modeling/data/X_2D.npy b/src/modules/applications/qml/generative_modeling/data/x_2d.npy similarity index 100% rename from src/modules/applications/qml/generative_modeling/data/X_2D.npy rename to src/modules/applications/qml/generative_modeling/data/x_2d.npy diff --git a/src/modules/applications/qml/generative_modeling/GenerativeModeling.py b/src/modules/applications/qml/generative_modeling/generative_modeling.py similarity index 97% rename from src/modules/applications/qml/generative_modeling/GenerativeModeling.py rename to src/modules/applications/qml/generative_modeling/generative_modeling.py index 8c61280ab..9f5506b3c 100644 --- a/src/modules/applications/qml/generative_modeling/GenerativeModeling.py +++ b/src/modules/applications/qml/generative_modeling/generative_modeling.py @@ -15,9 +15,9 @@ from typing import Union from utils import start_time_measurement, end_time_measurement -from modules.applications.qml.QML import QML -from modules.applications.qml.generative_modeling.data.data_handler.DiscreteData import DiscreteData -from modules.applications.qml.generative_modeling.data.data_handler.ContinuousData import ContinuousData +from modules.applications.qml.qml import QML +from modules.applications.qml.generative_modeling.data.data_handler.discrete_data import DiscreteData +from modules.applications.qml.generative_modeling.data.data_handler.continuous_data import ContinuousData class GenerativeModeling(QML): diff --git a/src/modules/applications/qml/generative_modeling/mappings/CustomQiskitNoisyBackend.py b/src/modules/applications/qml/generative_modeling/mappings/custom_qiskit_noisy_backend.py similarity index 98% rename from src/modules/applications/qml/generative_modeling/mappings/CustomQiskitNoisyBackend.py rename to src/modules/applications/qml/generative_modeling/mappings/custom_qiskit_noisy_backend.py index 43e49ec4d..e6d515637 100644 --- a/src/modules/applications/qml/generative_modeling/mappings/CustomQiskitNoisyBackend.py +++ b/src/modules/applications/qml/generative_modeling/mappings/custom_qiskit_noisy_backend.py @@ -25,9 +25,9 @@ from qiskit_aer import Aer, AerSimulator, noise from qiskit_aer.noise import NoiseModel -from modules.applications.qml.generative_modeling.training.QCBM import QCBM -from modules.applications.qml.generative_modeling.training.Inference import Inference -from modules.applications.qml.generative_modeling.mappings.LibraryGenerative import LibraryGenerative +from modules.applications.qml.generative_modeling.training.qcbm import QCBM +from modules.applications.qml.generative_modeling.training.inference import Inference +from modules.applications.qml.generative_modeling.mappings.library_generative import LibraryGenerative logging.getLogger("NoisyQiskit").setLevel(logging.WARNING) diff --git a/src/modules/applications/qml/generative_modeling/mappings/LibraryGenerative.py b/src/modules/applications/qml/generative_modeling/mappings/library_generative.py similarity index 93% rename from src/modules/applications/qml/generative_modeling/mappings/LibraryGenerative.py rename to src/modules/applications/qml/generative_modeling/mappings/library_generative.py index 54e6d9546..9c5d9c178 100644 --- a/src/modules/applications/qml/generative_modeling/mappings/LibraryGenerative.py +++ b/src/modules/applications/qml/generative_modeling/mappings/library_generative.py @@ -12,13 +12,13 @@ # See the License for the specific language governing permissions and # limitations under the License. -import logging from abc import ABC +import logging from typing import TypedDict -from modules.applications.qml.Model import Model -from modules.Core import Core -from utils import end_time_measurement, start_time_measurement +from utils import start_time_measurement, end_time_measurement +from modules.core import Core +from modules.applications.qml.model import Model class LibraryGenerative(Core, Model, ABC): diff --git a/src/modules/applications/qml/generative_modeling/mappings/LibraryPennylane.py b/src/modules/applications/qml/generative_modeling/mappings/library_pennylane.py similarity index 96% rename from src/modules/applications/qml/generative_modeling/mappings/LibraryPennylane.py rename to src/modules/applications/qml/generative_modeling/mappings/library_pennylane.py index 96f1e2c55..0184a39fd 100644 --- a/src/modules/applications/qml/generative_modeling/mappings/LibraryPennylane.py +++ b/src/modules/applications/qml/generative_modeling/mappings/library_pennylane.py @@ -19,10 +19,10 @@ import pennylane as qml from jax import numpy as jnp -from modules.applications.qml.generative_modeling.mappings.LibraryGenerative import LibraryGenerative -from modules.applications.qml.generative_modeling.training.Inference import Inference -from modules.applications.qml.generative_modeling.training.QGAN import QGAN -from modules.applications.qml.generative_modeling.training.QCBM import QCBM +from modules.applications.qml.generative_modeling.mappings.library_generative import LibraryGenerative +from modules.applications.qml.generative_modeling.training.inference import Inference +from modules.applications.qml.generative_modeling.training.qgan import QGAN +from modules.applications.qml.generative_modeling.training.qcbm import QCBM jax.config.update("jax_enable_x64", True) diff --git a/src/modules/applications/qml/generative_modeling/mappings/LibraryQiskit.py b/src/modules/applications/qml/generative_modeling/mappings/library_qiskit.py similarity index 96% rename from src/modules/applications/qml/generative_modeling/mappings/LibraryQiskit.py rename to src/modules/applications/qml/generative_modeling/mappings/library_qiskit.py index b870ca3ee..f10b251eb 100644 --- a/src/modules/applications/qml/generative_modeling/mappings/LibraryQiskit.py +++ b/src/modules/applications/qml/generative_modeling/mappings/library_qiskit.py @@ -21,10 +21,10 @@ from qiskit.providers import Backend from qiskit.quantum_info import Statevector -from modules.applications.qml.generative_modeling.training.QCBM import QCBM -from modules.applications.qml.generative_modeling.training.QGAN import QGAN -from modules.applications.qml.generative_modeling.training.Inference import Inference -from modules.applications.qml.generative_modeling.mappings.LibraryGenerative import LibraryGenerative +from modules.applications.qml.generative_modeling.training.qcbm import QCBM +from modules.applications.qml.generative_modeling.training.qgan import QGAN +from modules.applications.qml.generative_modeling.training.inference import Inference +from modules.applications.qml.generative_modeling.mappings.library_generative import LibraryGenerative logging.getLogger("qiskit").setLevel(logging.WARNING) @@ -50,7 +50,8 @@ def get_requirements() -> list[dict]: """ return [ {"name": "qiskit", "version": "1.3.0"}, - {"name": "numpy", "version": "1.26.4"} + {"name": "numpy", "version": "1.26.4"}, + {"name": "qiskit_aer", "version": "0.15.1"} ] def get_parameter_options(self) -> dict: @@ -201,7 +202,7 @@ def select_backend(config: str, n_qubits: int) -> any: backend = Aer.get_backend('statevector_simulator') backend.set_options(device="CPU") elif config == "ionQ_Harmony": - from modules.devices.braket.Ionq import Ionq # pylint: disable=C0415 + from modules.devices.braket.ionq import Ionq # pylint: disable=C0415 from qiskit_braket_provider import AWSBraketBackend, AWSBraketProvider # pylint: disable=C0415 device_wrapper = Ionq("ionQ", "arn:aws:braket:::device/qpu/ionq/ionQdevice") backend = AWSBraketBackend( @@ -213,7 +214,7 @@ def select_backend(config: str, n_qubits: int) -> any: backend_version="2", ) elif config == "Amazon_SV1": - from modules.devices.braket.SV1 import SV1 # pylint: disable=C0415 + from modules.devices.braket.sv1 import SV1 # pylint: disable=C0415 from qiskit_braket_provider import AWSBraketBackend, AWSBraketProvider # pylint: disable=C0415 device_wrapper = SV1("SV1", "arn:aws:braket:::device/quantum-simulator/amazon/sv1") backend = AWSBraketBackend( diff --git a/src/modules/applications/qml/generative_modeling/mappings/PresetQiskitNoisyBackend.py b/src/modules/applications/qml/generative_modeling/mappings/preset_qiskit_noisy_backend.py similarity index 98% rename from src/modules/applications/qml/generative_modeling/mappings/PresetQiskitNoisyBackend.py rename to src/modules/applications/qml/generative_modeling/mappings/preset_qiskit_noisy_backend.py index 844e3151b..9f5e96d20 100644 --- a/src/modules/applications/qml/generative_modeling/mappings/PresetQiskitNoisyBackend.py +++ b/src/modules/applications/qml/generative_modeling/mappings/preset_qiskit_noisy_backend.py @@ -23,9 +23,9 @@ from qiskit_aer.noise import NoiseModel from qiskit_ibm_runtime.fake_provider import FakeProviderForBackendV2 -from modules.applications.qml.generative_modeling.training.QCBM import QCBM -from modules.applications.qml.generative_modeling.training.Inference import Inference -from modules.applications.qml.generative_modeling.mappings.LibraryGenerative import LibraryGenerative +from modules.applications.qml.generative_modeling.training.qcbm import QCBM +from modules.applications.qml.generative_modeling.training.inference import Inference +from modules.applications.qml.generative_modeling.mappings.library_generative import LibraryGenerative logging.getLogger("NoisyQiskit").setLevel(logging.WARNING) diff --git a/src/modules/applications/qml/generative_modeling/metrics/MetricsGeneralization.py b/src/modules/applications/qml/generative_modeling/metrics/metrics_generalization.py similarity index 100% rename from src/modules/applications/qml/generative_modeling/metrics/MetricsGeneralization.py rename to src/modules/applications/qml/generative_modeling/metrics/metrics_generalization.py diff --git a/src/modules/applications/qml/generative_modeling/training/Inference.py b/src/modules/applications/qml/generative_modeling/training/inference.py similarity index 97% rename from src/modules/applications/qml/generative_modeling/training/Inference.py rename to src/modules/applications/qml/generative_modeling/training/inference.py index aa050eb06..a04714c9a 100644 --- a/src/modules/applications/qml/generative_modeling/training/Inference.py +++ b/src/modules/applications/qml/generative_modeling/training/inference.py @@ -15,7 +15,7 @@ from typing import TypedDict import numpy as np -from modules.applications.qml.generative_modeling.training.TrainingGenerative import TrainingGenerative, Core, GPU +from modules.applications.qml.generative_modeling.training.training_generative import TrainingGenerative, Core, GPU class Inference(TrainingGenerative): diff --git a/src/modules/applications/qml/generative_modeling/training/QCBM.py b/src/modules/applications/qml/generative_modeling/training/qcbm.py similarity index 99% rename from src/modules/applications/qml/generative_modeling/training/QCBM.py rename to src/modules/applications/qml/generative_modeling/training/qcbm.py index e665a6b6c..b8c9dc5a1 100644 --- a/src/modules/applications/qml/generative_modeling/training/QCBM.py +++ b/src/modules/applications/qml/generative_modeling/training/qcbm.py @@ -22,7 +22,7 @@ from matplotlib import pyplot as plt from tensorboardX import SummaryWriter -from modules.applications.qml.generative_modeling.training.TrainingGenerative import TrainingGenerative, Core, GPU +from modules.applications.qml.generative_modeling.training.training_generative import TrainingGenerative, Core, GPU from utils_mpi import is_running_mpi, get_comm matplotlib.use('Agg') diff --git a/src/modules/applications/qml/generative_modeling/training/QGAN.py b/src/modules/applications/qml/generative_modeling/training/qgan.py similarity index 99% rename from src/modules/applications/qml/generative_modeling/training/QGAN.py rename to src/modules/applications/qml/generative_modeling/training/qgan.py index a217a3d2c..8308888b3 100644 --- a/src/modules/applications/qml/generative_modeling/training/QGAN.py +++ b/src/modules/applications/qml/generative_modeling/training/qgan.py @@ -24,7 +24,7 @@ from torch import nn from torch.utils.data import DataLoader -from modules.applications.qml.generative_modeling.training.TrainingGenerative import TrainingGenerative, Core +from modules.applications.qml.generative_modeling.training.training_generative import TrainingGenerative, Core from utils_mpi import is_running_mpi, get_comm matplotlib.use('Agg') @@ -87,7 +87,7 @@ def get_requirements() -> list[dict]: """ return [ {"name": "numpy", "version": "1.26.4"}, - {"name": "torch", "version": "2.5.1"}, + {"name": "torch", "version": "2.2.2"}, {"name": "matplotlib", "version": "3.9.3"}, {"name": "tensorboard", "version": "2.18.0"}, {"name": "tensorboardX", "version": "2.6.2.2"} diff --git a/src/modules/applications/qml/generative_modeling/training/TrainingGenerative.py b/src/modules/applications/qml/generative_modeling/training/training_generative.py similarity index 97% rename from src/modules/applications/qml/generative_modeling/training/TrainingGenerative.py rename to src/modules/applications/qml/generative_modeling/training/training_generative.py index 2e2f756ec..7e45bb4ef 100644 --- a/src/modules/applications/qml/generative_modeling/training/TrainingGenerative.py +++ b/src/modules/applications/qml/generative_modeling/training/training_generative.py @@ -13,8 +13,8 @@ # limitations under the License. import logging -import time from abc import ABC +import time try: import cupy as np @@ -25,9 +25,9 @@ GPU = False logging.info("CuPy not available, using vanilla numpy, data processing on CPU") -from modules.applications.qml.Training import Training -from modules.Core import Core -from utils import end_time_measurement, start_time_measurement +from modules.core import Core +from modules.applications.qml.training import Training +from utils import start_time_measurement, end_time_measurement class TrainingGenerative(Core, Training, ABC): diff --git a/src/modules/applications/qml/generative_modeling/transformations/MinMax.py b/src/modules/applications/qml/generative_modeling/transformations/min_max.py similarity index 97% rename from src/modules/applications/qml/generative_modeling/transformations/MinMax.py rename to src/modules/applications/qml/generative_modeling/transformations/min_max.py index acf2ddaa6..1bd08157c 100644 --- a/src/modules/applications/qml/generative_modeling/transformations/MinMax.py +++ b/src/modules/applications/qml/generative_modeling/transformations/min_max.py @@ -16,9 +16,9 @@ import numpy as np -from modules.applications.qml.generative_modeling.transformations.Transformation import Transformation -from modules.applications.qml.generative_modeling.circuits.CircuitStandard import CircuitStandard -from modules.applications.qml.generative_modeling.circuits.CircuitCardinality import CircuitCardinality +from modules.applications.qml.generative_modeling.transformations.transformation import Transformation +from modules.applications.qml.generative_modeling.circuits.circuit_standard import CircuitStandard +from modules.applications.qml.generative_modeling.circuits.circuit_cardinality import CircuitCardinality class MinMax(Transformation): # pylint: disable=R0902 diff --git a/src/modules/applications/qml/generative_modeling/transformations/PIT.py b/src/modules/applications/qml/generative_modeling/transformations/pit.py similarity index 98% rename from src/modules/applications/qml/generative_modeling/transformations/PIT.py rename to src/modules/applications/qml/generative_modeling/transformations/pit.py index 415a137cf..23534ea18 100644 --- a/src/modules/applications/qml/generative_modeling/transformations/PIT.py +++ b/src/modules/applications/qml/generative_modeling/transformations/pit.py @@ -15,8 +15,8 @@ import numpy as np import pandas as pd -from modules.applications.qml.generative_modeling.transformations.Transformation import Transformation -from modules.applications.qml.generative_modeling.circuits.CircuitCopula import CircuitCopula +from modules.applications.qml.generative_modeling.transformations.transformation import Transformation +from modules.applications.qml.generative_modeling.circuits.circuit_copula import CircuitCopula class PIT(Transformation): # pylint disable=R0902 diff --git a/src/modules/applications/qml/generative_modeling/transformations/Transformation.py b/src/modules/applications/qml/generative_modeling/transformations/transformation.py similarity index 98% rename from src/modules/applications/qml/generative_modeling/transformations/Transformation.py rename to src/modules/applications/qml/generative_modeling/transformations/transformation.py index 1c958ada6..1a09d73c7 100644 --- a/src/modules/applications/qml/generative_modeling/transformations/Transformation.py +++ b/src/modules/applications/qml/generative_modeling/transformations/transformation.py @@ -12,13 +12,13 @@ # See the License for the specific language governing permissions and # limitations under the License. -from abc import ABC, abstractmethod from itertools import product +from abc import ABC, abstractmethod import numpy as np -from modules.Core import Core -from utils import end_time_measurement, start_time_measurement +from modules.core import Core +from utils import start_time_measurement, end_time_measurement class Transformation(Core, ABC): diff --git a/src/modules/applications/qml/metrics_quantum.py b/src/modules/applications/qml/metrics_quantum.py new file mode 100644 index 000000000..5e3235243 --- /dev/null +++ b/src/modules/applications/qml/metrics_quantum.py @@ -0,0 +1,86 @@ +# Copyright 2021 The QUARK Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from qiskit import QuantumCircuit +from modules.applications.qml.qleet.interface.circuit import CircuitDescriptor +from modules.applications.qml.qleet.analyzers.expressibility import Expressibility +from modules.applications.qml.qleet.analyzers.entanglement import EntanglementCapability +import os +import sys +from typing import Dict + +# Add the parent directory to the sys.path +sys.path.append(os.path.dirname(os.path.abspath(__file__))) + + +class MetricsQuantum: + """ + A class to compute quantum metrics for quantum circuits. + """ + + def __init__(self) -> None: + pass + + def get_metrics(self, circuit: QuantumCircuit, params: list) -> Dict[str, float]: + """ + Method that determines all classification metrics. + + :param circuit: Quantum circuit + :param params: Circuit parameters + :return: Dictionary with quantum metrics + """ + + results = { + "meyer-wallach": self.entanglement_meyer_wallach(circuit, params), + "expressibility_jsd": self.expressibility_jensen_shannon(circuit, params), + } + + return results + + def entanglement_meyer_wallach(self, circuit: QuantumCircuit, params: list, samples=100) -> float: + """ + Method to determine the Meyer-Wallach entanglement. + + :param circuit: Quantum circuit + :param params: Circuit parameters + :param samples: Samples used to obtain metric + :return: Entanglement + """ + + qiskit_descriptor = CircuitDescriptor( + circuit=circuit, params=params, cost_function=None + ) + qiskit_entanglement_capability = EntanglementCapability( + qiskit_descriptor, samples=samples + ) + entanglement = qiskit_entanglement_capability.entanglement_capability( + "meyer-wallach" + ) + return entanglement + + def expressibility_jensen_shannon(self, circuit: QuantumCircuit, params: list) -> float: + """ + Method to determine the Jensen–Shannon Divergence metric. + + :param circuit: Quantum circuit + :param params: Circuit parameters + :return: Expressibility + """ + + qiskit_descriptor = CircuitDescriptor( + circuit=circuit, params=params, cost_function=None + ) + qiskit_expressibility = Expressibility(qiskit_descriptor, samples=100) + expr = qiskit_expressibility.expressibility("jsd") + return expr diff --git a/src/modules/applications/qml/Model.py b/src/modules/applications/qml/model.py similarity index 100% rename from src/modules/applications/qml/Model.py rename to src/modules/applications/qml/model.py diff --git a/src/modules/applications/qml/qleet/__init__.py b/src/modules/applications/qml/qleet/__init__.py new file mode 100644 index 000000000..0664f3da9 --- /dev/null +++ b/src/modules/applications/qml/qleet/__init__.py @@ -0,0 +1,15 @@ +# Copyright 2021 The QUARK Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +"""Module containint qLEET functions""" diff --git a/src/modules/applications/qml/qleet/analyzers/__init__.py b/src/modules/applications/qml/qleet/analyzers/__init__.py new file mode 100644 index 000000000..b1144dc8b --- /dev/null +++ b/src/modules/applications/qml/qleet/analyzers/__init__.py @@ -0,0 +1,15 @@ +# Copyright 2021 The QUARK Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +""" Module containing qLeet analyzers""" diff --git a/src/modules/applications/qml/qleet/analyzers/entanglement.py b/src/modules/applications/qml/qleet/analyzers/entanglement.py new file mode 100644 index 000000000..74619a984 --- /dev/null +++ b/src/modules/applications/qml/qleet/analyzers/entanglement.py @@ -0,0 +1,187 @@ +# Copyright 2021 The QUARK Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +"""Module to evaluate the achievable entanglement in circuits.""" + +import itertools +import typing + +from qiskit_aer.noise import NoiseModel as qiskitNoiseModel + +from qiskit.quantum_info import partial_trace +from scipy.special import comb + +import numpy as np + +from ..interface.metas import MetaExplorer +from ..interface.circuit import CircuitDescriptor +from ..simulators.circuit_simulators import CircuitSimulator + +NOISE_MODELS = { + "qiskit": qiskitNoiseModel, +} + + +class EntanglementCapability(MetaExplorer): + """Calculates entangling capability of a parameterized quantum circuit""" + + def __init__( + self, + circuit: CircuitDescriptor, + noise_model: typing.Union[ + qiskitNoiseModel, None + ] = None, + samples: int = 1000, + ): + """Constructor for entanglement capability plotter + + :param circuit: input circuit as a CircuitDescriptor object + :param noise_model: (dict, NoiseModel) initialization noise-model dictionary for + generating noise model + :param samples: number of samples for the experiment + :returns Entanglement object instance + :raises ValueError: If circuit and noise model does not correspond to same framework + """ + super().__init__() + self.circuit = circuit + + if noise_model is not None: + if ( + ( + circuit.default_backend == "qiskit" + and isinstance(noise_model, qiskitNoiseModel) + ) + ): + self.noise_model = noise_model + else: + raise ValueError( + f"Circuit and noise model must correspond to the same \ + framework but circuit:{circuit.default_backend} and \ + noise_model:{type(noise_model)} were provided." + ) + else: + self.noise_model = None + + self.num_samples = samples + + def gen_params(self) -> typing.Tuple[typing.List, typing.List]: + """Generate parameters for the calculation of expressibility + + :return theta (np.array): first list of parameters for the parameterized quantum circuit + :return phi (np.array): second list of parameters for the parameterized quantum circuit + """ + theta = [ + {p: 2 * np.random.random() * np.pi for p in self.circuit.parameters} + for _ in range(self.num_samples) + ] + phi = [ + {p: 2 * np.random.random() * np.pi for p in self.circuit.parameters} + for _ in range(self.num_samples) + ] + return theta, phi + + @staticmethod + def scott_helper(state, perms): + """Helper function for entanglement measure. It gives trace of the output state""" + dems = np.linalg.matrix_power( + [partial_trace(state, list(qb)).data for qb in perms], 2 + ) + trace = np.trace(dems, axis1=1, axis2=2) + return np.sum(trace).real + + def meyer_wallach_measure(self, states, num_qubits): + r"""Returns the meyer-wallach entanglement measure for the given circuit. + + .. math:: + Q = \frac{2}{|\vec{\theta}|}\sum_{\theta_{i}\in \vec{\theta}} + \Bigg(1-\frac{1}{n}\sum_{k=1}^{n}Tr(\rho_{k}^{2}(\theta_{i}))\Bigg) + + """ + permutations = list(itertools.combinations(range(num_qubits), num_qubits - 1)) + ns = 2 * sum( + [ + 1 - 1 / num_qubits * self.scott_helper(state, permutations) + for state in states + ] + ) + return ns.real + + def scott_measure(self, states, num_qubits): + r"""Returns the scott entanglement measure for the given circuit. + + .. math:: + Q_{m} = \frac{2^{m}}{(2^{m}-1) |\vec{\theta}|}\sum_{\theta_i \in \vec{\theta}}\ + \bigg(1 - \frac{m! (n-m)!)}{n!}\sum_{|S|=m} \text{Tr} (\rho_{S}^2 (\theta_i)) \bigg)\ + \quad m= 1, \ldots, \lfloor n/2 \rfloor + + """ + m = range(1, num_qubits // 2 + 1) + permutations = [ + list(itertools.combinations(range(num_qubits), num_qubits - idx)) + for idx in m + ] + combinations = [1 / comb(num_qubits, idx) for idx in m] + contributions = [2**idx / (2**idx - 1) for idx in m] + ns = [] + + for ind, perm in enumerate(permutations): + ns.append( + contributions[ind] + * sum( + [ + 1 - combinations[ind] * self.scott_helper(state, perm) + for state in states + ] + ) + ) + + return np.array(ns) + + def entanglement_capability( + self, measure: str = "meyer-wallach", shots: int = 1024 + ) -> float: + """Returns entanglement measure for the given circuit + + :param measure: specification for the measure used in the entangling capability + :param shots: number of shots for circuit execution + :returns pqc_entangling_capability (float): entanglement measure value + :raises ValueError: if invalid measure is specified + """ + thetas, phis = self.gen_params() + + theta_circuits = [ + CircuitSimulator(self.circuit, self.noise_model).simulate(theta, shots) + for theta in thetas + ] + phi_circuits = [ + CircuitSimulator(self.circuit, self.noise_model).simulate(phi, shots) + for phi in phis + ] + + num_qubits = self.circuit.num_qubits + + if measure == "meyer-wallach": + pqc_entanglement_capability = self.meyer_wallach_measure( + theta_circuits + phi_circuits, num_qubits + ) / (2 * self.num_samples) + elif measure == "scott": + pqc_entanglement_capability = self.scott_measure( + theta_circuits + phi_circuits, num_qubits + ) / (2 * self.num_samples) + else: + raise ValueError( + "Invalid measure provided, choose from 'meyer-wallach' or 'scott'" + ) + + return pqc_entanglement_capability diff --git a/src/modules/applications/qml/qleet/analyzers/entanglement_spectrum.py b/src/modules/applications/qml/qleet/analyzers/entanglement_spectrum.py new file mode 100644 index 000000000..671bcf070 --- /dev/null +++ b/src/modules/applications/qml/qleet/analyzers/entanglement_spectrum.py @@ -0,0 +1,258 @@ +# Copyright 2021 The QUARK Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +"""Module to evaluate the entanglement spectrum of circuits.""" + +from ..simulators.circuit_simulators import CircuitSimulator +from ..interface.circuit import CircuitDescriptor +from ..interface.metas import MetaExplorer +import scipy as sp +import numpy as np +from matplotlib import pyplot as plt +import typing + +from qiskit_aer.noise import NoiseModel as qiskitNoiseModel + +from qiskit.quantum_info import partial_trace +from scipy.spatial.distance import jensenshannon + +import matplotlib +matplotlib.use("Agg") # Use a non-interactive backend for matplotlib + + +NOISE_MODELS = { + "qiskit": qiskitNoiseModel, +} + + +class EntanglementSpectrum(MetaExplorer): + """Calculates entanglement spectrum of a parameterized quantum circuit""" + + def __init__( + self, + circuit: CircuitDescriptor, + noise_model: typing.Union[ + qiskitNoiseModel, None + ] = None, + samples: int = 1000, + tapered_indices: tuple = tuple(), + cutoff: int = -30, + ): + """Constructor the the Expresssibility analyzer + + :param circuit: input circuit as a CircuitDescriptor object + :param noise_model: (dict, NoiseModel) initialization noise-model dictionary + :param samples: number of samples for the experiment + :param tapered_indices: qubits to be tapered for bipartiting the system + :param cutoff: minimum cutoff value for the eigenvalues + :raises ValueError: If circuit and noise model does not correspond to same framework + """ + super().__init__() + self.circuit = circuit + + if noise_model is not None: + if ( + ( + circuit.default_backend == "qiskit" + and isinstance(noise_model, qiskitNoiseModel) + ) + ): + self.noise_model = noise_model + else: + raise ValueError( + f"Circuit and noise model must correspond to the same \ + framework but circuit:{circuit.default_backend} and \ + noise_model:{type(noise_model)} were provided." + ) + else: + self.noise_model = None + + self.num_samples = samples + self.ent_spec = 0.0 + self.cutoff = cutoff + if tapered_indices: + if len(set(tapered_indices)) != self.circuit.num_qubits // 2: + raise ValueError( + f"The provided tapered_indices must exactly have half \ + the number of total qubits present in the system." + ) + self.tapered_indices = tapered_indices + else: + self.tapered_indices = tuple( + range(self.circuit.num_qubits // 2, self.circuit.num_qubits) + ) + self.eigvals_sample: typing.List[float] = [] + self.plot_data: typing.List[np.ndarray] = [] + + @staticmethod + def kl_divergence(prob_a: np.ndarray, prob_b: np.ndarray) -> float: + """Returns KL divergence between two probabilities""" + prob_a[prob_a == 0] = 1e-10 + kl_div = np.sum(np.where(prob_a != 0, prob_a * np.log(prob_a / prob_b), 0)) + return typing.cast(float, kl_div) + + @staticmethod + def marchenko_pastur_pdf(x, gamma): + """Computes the probability density function (PDF) for the Marchenko-Pastur distribution""" + lambda_plus = np.power(1 + np.sqrt(1 / gamma), 2) # Largest eigenvalue + lambda_minus = np.power(1 - np.sqrt(1 / gamma), 2) # Smallest eigenvalue + x_gamma_geq = np.maximum(lambda_plus - x, np.zeros_like(x)) + x_gamma_leq = np.maximum(x - lambda_minus, np.zeros_like(x)) + + return np.sqrt(x_gamma_geq * x_gamma_leq) / (2 * np.pi * gamma * x) + + @staticmethod + def inverse_transform_sampling(data, n_bins=1000, n_samples=1000): + """Samples from a given distribution followed by a given set of data""" + hist, bin_edges = np.histogram(data, bins=n_bins, density=True) + cum_values = np.zeros(bin_edges.shape) + cum_values[1:] = np.cumsum(hist * np.diff(bin_edges)) + return sp.interpolate.interp1d(cum_values, bin_edges)(np.random.rand(n_samples)) + + def gen_params(self) -> typing.List[typing.Dict[typing.Any, typing.Any]]: + """Generate parameters for the calculation of expressibility + + :returns theta: first list of parameters for the parameterized quantum circuit + """ + theta = [ + {p: 2 * np.random.random() * np.pi for p in self.circuit.parameters} + for _ in range(self.num_samples) + ] + return theta + + def prob_pqc(self, shots: int = 1024) -> typing.Tuple[np.ndarray, np.ndarray]: + """Return probability density function of fidelities for PQC + + :param shots: number of shots for circuit execution + :returns eigvals (np.array): np.array of all eigenvalues + :returns mean_eigvals (np.array): np.array of sample-wise mean of all eigenvalues + """ + thetas = self.gen_params() + theta_circuits = [ + CircuitSimulator(self.circuit, self.noise_model).simulate(theta, shots) + for theta in thetas + ] + rho_circs = [ + -sp.linalg.logm(partial_trace(rho, self.tapered_indices).data) + for rho in theta_circuits + ] + eigvals = [np.round(np.sort(np.linalg.eigvals(rho)), 5) for rho in rho_circs] + mean_eigvals = -np.mean(eigvals, axis=0) + mean_eigvals[np.where(mean_eigvals < self.cutoff)[0]] = self.cutoff + self.eigvals_sample = mean_eigvals + return np.array(eigvals), mean_eigvals + + def entanglement_spectrum( + self, measure: str = "kld", shots: int = 1024 + ) -> typing.Tuple[float, np.ndarray]: + r"""Returns entanglement spectrum divergence (ESD) for the circuit against + Marchenko-Pastur distribution + + .. math:: + ESD = D_{KL}(\hat{P}_{PQC}(H_{\text{ent}}; \theta) | P_{Haar}(H_{\text{ent}}))\\ + ESD = D_{\sqrt{JSD}}(\hat{P}_{PQC}(H_{\text{ent}}; \theta) | P_{Haar}(H_{\text{ent}})) + + :param measure: specifies measure used in the entanglement spectrum divergence calculation + :param shots: number of shots for circuit execution + :returns pqc_esd: float, entanglement spectrum divergence value + :raises ValueError: if invalid measure is specified + """ + + num_rows, num_cols = 2 ** (self.circuit.num_qubits // 2), 2 ** ( + self.circuit.num_qubits // 2 + ) + + if len(self.circuit.parameters) > 0: + eigvals, mean_eigvals = self.prob_pqc(shots) + else: + cor = np.corrcoef(np.random.normal(0, 1, size=(num_rows, num_cols))) + eigvals = np.array([np.linalg.eig(cor)[0]] * 10000) + mean_eigvals = -np.mean(eigvals, axis=0) + mean_eigvals[np.where(mean_eigvals < self.cutoff)[0]] = self.cutoff + + gamma = 1 + x_min = np.power(1 - np.sqrt(1 / gamma), 2) + x_min = 1e-1 if x_min < 1e-1 else x_min + x_max = np.power(1 + np.sqrt(1 / gamma), 2) + x = np.linspace(x_min, x_max, 1000) + + haar_prob = self.marchenko_pastur_pdf(x, gamma) + haar_prob /= np.sum(haar_prob) + + bin_edges: np.ndarray + pqc_hist, bin_edges = np.histogram(eigvals.real, 1000, density=True) + pqc_prob: np.ndarray = pqc_hist / float(pqc_hist.sum()) + + pqc_prob[np.where(pqc_prob == 0.0)[0]] = 1e-9 + haar_prob[np.where(haar_prob == 0.0)[0]] = 1e-9 + + if measure == "kld": + pqc_esd = self.kl_divergence(pqc_prob, haar_prob) + elif measure == "jsd": + pqc_esd = jensenshannon(pqc_prob, haar_prob, 2.0) + else: + raise ValueError("Invalid measure provided, choose from 'kld' or 'jsd'") + + mpd = np.array(self.inverse_transform_sampling(haar_prob, 10000, 256)).astype( + float + ) + self.plot_data = [mpd, bin_edges] + self.ent_spec = pqc_esd + + return pqc_esd, mean_eigvals + + def plot(self, data, figsize=(6, 4), dpi=300, **kwargs): + """Returns plot for expressibility visualization""" + + num_rows = 2 ** (self.circuit.num_qubits // 2) + gamma = 1 + x_min = np.power(1 - np.sqrt(1 / gamma), 2) + x_min = 1e-1 if x_min < 1e-1 else x_min + x_max = np.power(1 + np.sqrt(1 / gamma), 2) + x = np.linspace(x_min, x_max, 1000) + + mpd = np.array( + self.inverse_transform_sampling( + self.marchenko_pastur_pdf(x, gamma), 10000, num_rows + ) + ).astype(float) + fcol_min = np.min(np.array(data)[:, 0]).real + + ticks = np.arange(1, len(data) + 1) + cmap = plt.get_cmap("turbo", len(data)) + norm = matplotlib.colors.BoundaryNorm(np.arange(len(data) + 1) + 0.5, len(data)) + smap = plt.cm.ScalarMappable(norm=norm, cmap=cmap) + + fig = plt.figure(figsize=(12, 8), facecolor="white") + for idx, layer in enumerate(data): + plt.plot(range(len(layer)), layer.real, c=cmap(idx)) + + plt.plot( + range(len(mpd)), + np.sort(-mpd)[::-1] + fcol_min, + "--", + label="Marchenko-Pastur\n distribution", + c="black", + ) + + plt.legend(fontsize=12, loc="upper right") + plt.ylabel(r"$\xi_k$", fontsize=16) + plt.xlabel("k", fontsize=16) + plt.xticks(fontsize=14) + plt.yticks(fontsize=14) + + # add color bar + fig.colorbar(smap, ticks=ticks) + + return fig diff --git a/src/modules/applications/qml/qleet/analyzers/expressibility.py b/src/modules/applications/qml/qleet/analyzers/expressibility.py new file mode 100644 index 000000000..f047b8787 --- /dev/null +++ b/src/modules/applications/qml/qleet/analyzers/expressibility.py @@ -0,0 +1,255 @@ +# Copyright 2021 The QUARK Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +"""Module to evaluate the expressibility of circuits.""" + +from ..simulators.circuit_simulators import CircuitSimulator +from ..interface.circuit import CircuitDescriptor +from ..interface.metas import MetaExplorer +import numpy as np +from matplotlib import pyplot as plt +import itertools +# import typing +from typing import Union, List, Tuple, cast + +from qiskit_aer.noise import NoiseModel as qiskitNoiseModel + +from qiskit.quantum_info import state_fidelity +from scipy.spatial.distance import jensenshannon + +import matplotlib +matplotlib.use("Agg") # Use a non-interactive backend for matplotlib + + +NOISE_MODELS = { + "qiskit": qiskitNoiseModel, +} + + +class Expressibility(MetaExplorer): + """Calculates expressibility of a parameterized quantum circuit""" + + def __init__( + self, + circuit: CircuitDescriptor, + noise_model: Union[ + qiskitNoiseModel, None + ] = None, + samples: int = 1000, + ): + """Constructor the the Expressibility analyzer + + :param circuit: input circuit as a CircuitDescriptor object + :param noise_model: (dict, NoiseModel) initialization noise-model dictionary + :param samples: number of samples for the experiment + :raises ValueError: If circuit and noise model does not correspond to same framework + """ + super().__init__() + self.circuit = circuit + + if noise_model is not None: + if ( + ( + circuit.default_backend == "qiskit" + and isinstance(noise_model, qiskitNoiseModel) + ) + ): + self.noise_model = noise_model + else: + raise ValueError( + f"Circuit and noise model must correspond to the same \ + framework but circuit:{circuit.default_backend} and \ + noise_model:{type(noise_model)} were provided." + ) + else: + self.noise_model = None + + self.num_samples = samples + self.expr = 0.0 + self.plot_data: List[np.ndarray] = [] + + @staticmethod + def kl_divergence(prob_a: np.ndarray, prob_b: np.ndarray) -> float: + """Returns KL divergence between two probabilities""" + prob_a[prob_a == 0] = 1e-10 + kl_div = np.sum(np.where(prob_a != 0, prob_a * np.log(prob_a / prob_b), 0)) + return cast(float, kl_div) + + def gen_params(self) -> Tuple[List, List]: + """Generate parameters for the calculation of expressibility + + :returns theta (np.array): first list of parameters for the parameterized quantum circuit + :returns phi (np.array): second list of parameters for the parameterized quantum circuit + """ + theta = [ + {p: 2 * np.random.random() * np.pi for p in self.circuit.parameters} + for _ in range(self.num_samples) + ] + phi = [ + {p: 2 * np.random.random() * np.pi for p in self.circuit.parameters} + for _ in range(self.num_samples) + ] + return theta, phi + + def prob_haar(self) -> np.ndarray: + """Returns probability density function of fidelities for Haar Random States""" + fidelity = np.linspace(0, 1, self.num_samples) + num_qubits = self.circuit.num_qubits + return (2**num_qubits - 1) * (1 - fidelity + 1e-8) ** (2**num_qubits - 2) + + def prob_pqc(self, shots: int = 1024) -> np.ndarray: + """Return probability density function of fidelities for PQC + + :param shots: number of shots for circuit execution + :returns fidelities (np.array): np.array of fidelities + """ + thetas, phis = self.gen_params() + + theta_circuits = [ + CircuitSimulator(self.circuit, self.noise_model).simulate(theta, shots) + for theta in thetas + ] + phi_circuits = [ + CircuitSimulator(self.circuit, self.noise_model).simulate(phi, shots) + for phi in phis + ] + fidelity = np.array( + [ + state_fidelity(rho_a, rho_b) + for rho_a, rho_b in itertools.product(theta_circuits, phi_circuits) + ] + ) + return np.array(fidelity) + + def expressibility(self, measure: str = "kld", shots: int = 1024) -> float: + r"""Returns expressibility for the circuit + + .. math:: + Expr = D_{KL}(\hat{P}_{PQC}(F; \theta) | P_{Haar}(F))\\ + Expr = D_{\sqrt{JSD}}(\hat{P}_{PQC}(F; \theta) | P_{Haar}(F)) + + :param measure: specification for the measure used in the expressibility calculation + :param shots: number of shots for circuit execution + :returns pqc_expressibility: float, expressibility value + :raises ValueError: if invalid measure is specified + """ + haar = self.prob_haar() + haar_prob: np.ndarray = haar / float(haar.sum()) + + if len(self.circuit.parameters) > 0: + fidelity = self.prob_pqc(shots) + else: + fidelity = np.ones(self.num_samples**2) + + bin_edges: np.ndarray + pqc_hist, bin_edges = np.histogram( + fidelity, self.num_samples, range=(0, 1), density=True + ) + pqc_prob: np.ndarray = pqc_hist / float(pqc_hist.sum()) + + if measure == "kld": + pqc_expressibility = self.kl_divergence(pqc_prob, haar_prob) + elif measure == "jsd": + pqc_expressibility = 1 - jensenshannon(pqc_prob, haar_prob, 2.0) + else: + raise ValueError("Invalid measure provided, choose from 'kld' or 'jsd'") + self.plot_data = [haar_prob, pqc_prob, bin_edges] + self.expr = pqc_expressibility + + return pqc_expressibility + + def compare_expressibility( + self, circuit: Union[CircuitDescriptor, List[CircuitDescriptor]], measure: str = "kld", shots: int = 1024) -> List[float]: + r"""Compares expressibility against the provided circuit + + .. math:: + Expr = D_{KL}(\hat{P}_{PQC_1}(F; \theta) | \hat{P}_{PQC_2}(F; \theta))\\ + Expr = D_{\sqrt{JSD}}(\hat{P}_{PQC_1}(F; \theta) | \hat{P}_{PQC_2}(F; \theta)) + + :param measure: specification for the measure used in the expressibility calculation + :param shots: number of shots for circuit execution + :returns pqc_expressibility: float, expressibility value + :raises ValueError: if invalid measure is specified + """ + + thetas, phis = self.gen_params() + fidelities = [] + pqc_probs = [] + + if not isinstance(circuit, list) and isinstance(circuit, CircuitDescriptor): + circuit = [circuit] + + for circ in [*circuit, self.circuit]: + + if len(circuit.parameters) > 0: + theta_circuits = [ + CircuitSimulator(circ, self.noise_model).simulate(theta, shots) + for theta in thetas + ] + phi_circuits = [ + CircuitSimulator(circ, self.noise_model).simulate(phi, shots) + for phi in phis + ] + fidelity = np.array( + [ + state_fidelity(rho_a, rho_b) + for rho_a, rho_b in itertools.product(theta_circuits, phi_circuits) + ] + ) + else: + fidelity = np.ones(self.num_samples**2) + + fidelities.append(fidelity) + bin_edges: np.ndarray + pqc_hist, bin_edges = np.histogram( + fidelity, self.num_samples, range=(0, 1), density=True + ) + pqc_prob: np.ndarray = pqc_hist / float(pqc_hist.sum()) + pqc_probs.append(pqc_probs) + + pqc_expressibilities = [] + for pqc_prob in pqc_probs[:-1]: + if measure == "kld": + pqc_expressibility = self.kl_divergence(pqc_prob, pqc_probs[-1]) + elif measure == "jsd": + pqc_expressibility = jensenshannon(pqc_prob, pqc_probs[-1], 2.0) + else: + raise ValueError("Invalid measure provided, choose from 'kld' or 'jsd'") + pqc_expressibilities.appned(pqc_expressibility) + + return pqc_expressibilities + + def plot(self, figsize=(6, 4), dpi=300, **kwargs): + """Returns plot for expressibility visualization""" + if not self.plot_data: + raise ValueError("Perform expressibility calculation first") + + haar_prob, pqc_prob, bin_edges = self.plot_data + expr = self.expr + + bin_middles = (bin_edges[1:] + bin_edges[:-1]) / 2.0 + bin_width = bin_edges[1] - bin_edges[0] + + fig = plt.figure(figsize=figsize, dpi=dpi, **kwargs) + plt.bar(bin_middles, haar_prob, width=bin_width, label="Haar") + plt.bar(bin_middles, pqc_prob, width=bin_width, label="PQC", alpha=0.6) + plt.xlim((-0.05, 1.05)) + plt.ylim(bottom=0.0, top=max(max(pqc_prob), max(haar_prob)) + 0.01) + plt.grid(True) + plt.title(f"Expressibility: {np.round(expr, 5)}") + plt.xlabel("Fidelity") + plt.ylabel("Probability") + plt.legend() + + return fig diff --git a/src/modules/applications/qml/qleet/analyzers/histogram.py b/src/modules/applications/qml/qleet/analyzers/histogram.py new file mode 100644 index 000000000..5ddf8b1e9 --- /dev/null +++ b/src/modules/applications/qml/qleet/analyzers/histogram.py @@ -0,0 +1,148 @@ +# Copyright 2021 The QUARK Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +"""Module responsible to generating histogram plots of the parameter values. + +This modules is our alternative for TensorBoard histograms. It shows the 3D histograms for +one parameter over an ensemble of models or that for a group of parameters. As the models trains +those histograms should change from the initial distribution they were initialized to, to the +final optimal distribution they converge to. The parameter which don't really change their +distribution in training may be either not contributing to the model or are not being trained +well. +""" + +import typing +import numpy as np + +import sympy +from matplotlib import pyplot as plt +import seaborn as sns + +from ..interface.metas import MetaExplorer +from ..interface.circuit import CircuitDescriptor +from ..simulators.pqc_trainer import PQCSimulatedTrainer + + +class ParameterHistograms(MetaExplorer): + """Class to plot the histograms of parameters in the circuit.""" + + def __init__( + self, + circuit: CircuitDescriptor, + ensemble_size: int = 3, + groups: typing.Optional[typing.Dict[str, typing.List[sympy.Symbol]]] = None, + epochs_chart=(0, 10, 10), + ) -> None: + """Creates an explorer object which will plot the histogram. + We specify which parameter will be grouped together to be plotted in the same histogram, + as well as after how many epochs do we want to draw the plots. + + :type circuit: CircuitDescriptor + :param circuit: The Parametrized Quantum circuit + :type grous: Dict mapping strings to a list of `sympy.Symbol`s + :param groups: Groups of variables which can be analyzed together. + :type ensemble_size: int + :param ensemble_size: The number of models in the ensemble + :type epochs_chart: tuple of int + :param epochs_chart: The list of number of epochs in each block + + The epochs chart presents the number of iterations of training in each block, after each + block of all the models we shall plot the histograms of the parameters, so the plotting + is not done after every single epochs and the spacing is left customizable to the user. + + The parameter groups have associated group names which are the keys of the dictionary, we + use them to label the plots. + + TODO: Get the trainable model class as an input, convert this to a logger + """ + super().__init__() + self.circuit = circuit + # Generate an ensemble or runs + self.ensemble_size = ensemble_size + self.models = [ + PQCSimulatedTrainer(self.circuit) for _ in range(self.ensemble_size) + ] + self.epochs_chart = epochs_chart + # Prepare the groups of variables which will be analyzed together + if groups is not None: + self.groups = groups + else: + self.groups = dict() + for param in circuit.parameters: + self.groups[param.name] = [param] + # Prepare the array to store histograms resulting from simulation + self._histograms: typing.Dict[str, typing.List[typing.List]] = { + group: [[] for _ in self.epochs_chart] for group in self.groups.keys() + } + + def simulate(self) -> None: + """Simulates the circuit and generate the histogram data. + + This is training an ensemble of models for the same number of epochs, + which is extracted from the epochs chart property. After each block of training + of all the models, the parameter are extracted and stored to be plotted later. + """ + for epochs_idx, epochs_to_train in enumerate(self.epochs_chart): + for model in self.models: + model.train(n_samples=epochs_to_train) + for group_name, group_symbols in self.groups.items(): + for model in self.models: + for variable in group_symbols: + value = self._get_symbol_value_from_model(model, variable) + self._histograms[group_name][epochs_idx].append(value) + print(f"{variable} in {epochs_idx}: {value}") + + @staticmethod + def _get_symbol_value_from_model( + model: PQCSimulatedTrainer, symbol: sympy.Symbol + ) -> float: + """Get the current value of the symbol in the PQC Trainer + + :type model: PQCSimulatedTrainer + :param model: The model we want to find the symbol values from + :type sybmol: sympy.Symbol + :param symbol: The sympy symbol we want to find the value of + :return: The current value of the symbol + :rtype: float + """ + return model.pqc_layer.symbol_values()[symbol] + + def plot(self) -> np.ndarray: + """Plot the parameter histogram for this circuit. + The plots are layed out with the different epochs of training along one axis and + the different parameter groups on the other. All the values of the same parameter + group for the same epoch block over all the models are in the ensemble are plotted + in a single histogram. + + :return: The axes with the completed plots + :rtype: plt.Axes + + Try to ensure that a substantial number of parameters are part of each histogram, + so use large groups or train more models in the ensemble to make the plots meaningful. + """ + self.simulate() + _fig, ax = plt.subplots( + len(self.epochs_chart), + len(self.groups), + figsize=(len(self.groups) * 5, len(self.epochs_chart) * 5), + ) + for group_idx, group_name in enumerate(self.groups.keys()): + for epoch_idx in range(len(self.epochs_chart)): + sns.kdeplot( + self._histograms[group_name][epoch_idx], + ax=ax[epoch_idx, group_idx], + ) + ax[epoch_idx, group_idx].set_title(f"{group_name} @ epoch:{epoch_idx}") + ax[epoch_idx, group_idx].set_xlabel("Parameter Values") + return ax diff --git a/src/modules/applications/qml/qleet/analyzers/loss_landscape.py b/src/modules/applications/qml/qleet/analyzers/loss_landscape.py new file mode 100644 index 000000000..272a8b28a --- /dev/null +++ b/src/modules/applications/qml/qleet/analyzers/loss_landscape.py @@ -0,0 +1,178 @@ +# Copyright 2021 The QUARK Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +"""Module to plot the loss landscapes of circuits. + +For any variational quantum algorithm being trained to optimize on a given metric, +the plot of a projected subspace of the metric is of value because it helps us +confirm along random axes that our point is indeed the local minima / maxima and +also helps visualize how rough the landscape is giving clues on how likely the +variational models might converge. + +We hope that these visualizations can help improve the choice of optimizers and +ansatz we have for these quantum circuits. +""" + +import typing as ty + +import numpy as np +import tqdm.auto as tqdm +import plotly.graph_objects as pg + +from ..simulators.pqc_trainer import PQCSimulatedTrainer +from ..interface.metric_spec import MetricSpecifier +from ..interface.metas import MetaExplorer + + +class LossLandscapePlotter(MetaExplorer): + """This class plots the loss landscape for a given PQC trainer object. + + It can plot the true loss that we are training on or on some other metric, this can help + use proxy metrics as loss functions and seeing if they help optimize on the true target + metric. + + These plots can support 1-D and 2-D subspace projections for now, since we have to plot + the loss value on the second or third axis. A 3-D projection of the plot will also be supported + by v1.0.0 and onwards, which will use colors and point density to show the metric values. + """ + + def __init__( + self, solver: PQCSimulatedTrainer, metric: MetricSpecifier, dim: int = 2 + ) -> None: + """Initializes the Loss Landscape plotter. + The plotter takes a PQC trainer, which will expose the it's present parameters + and help us sample the outputs of the circuit, be it classical or use the quantum state + vectors or density matrices, and through those outputs it computes our metric to give + the 3D contour plot of the metric for perturbations of the parameters near the currently + trained optima. + + :type solver: PQCSimulatedTrainer + :param solver: The PQC trainer class, which contains both the + :type metric: MetricSpecifier + :param metric: The metric which is being plotted for different parameter values + :type dim: int + :param dim: The number of dimensions of the subspace to be sampled, + necessarily 2 to get a contour plot + """ + super().__init__() + self.n = len(solver.circuit.parameters) + self.metric = metric + self.solver = solver + self.dim = dim + self.axes = self.__random_subspace(dim=self.dim) + + def __random_subspace(self, dim: int) -> np.ndarray: + """Generates basis vectors for a random subspace + Performs Gram-Schmidt orthonormalization to generate this set. + + :type dim: int + :param dim: The number of dimensions the subspace should have + :returns: The basis set of vectors for our subspace as a 2D numpy matrix + + Note that this only works for Real valued vectors, there are issues with doing this for + complex vectors to generate unitary matrices, use a different approach for that. + """ + axes: ty.List[np.ndarray] = [] + for _i in range(dim): + axis = np.random.random(self.n) + for other_axis in axes: + projection = np.dot(axis, other_axis) + axis = axis - projection * other_axis + axis = axis / np.linalg.norm(axis) + axes.append(axis) + return np.stack(axes, axis=0) + + def scan( + self, points: int, distance: float, origin: np.ndarray + ) -> ty.Tuple[np.ndarray, np.ndarray]: + """Scans the target vector-subspace for values of the metric + Returns the sampled coordinates in the grid and the values of the metric at those + coordinates. The sampling of the subspace is done uniformly, and evenly in all directions. + + :type points: int + :param points: Number of points to sample + :type distance: float + :param distance: The range of parameters around the current value to scan over + :type origin: np.ndarray + :param origin: The value of the current parameter to be used as origin of our plot + :returns: tuple of the coordinates and the metric values at those coordinates + :rtype: a tuple of np.array, shapes being (n, dims) and (n,) + """ + chained_range = [ + np.linspace(-distance, distance, points) for _i in range(self.dim) + ] + coords = np.reshape( + np.stack(np.meshgrid(*chained_range), axis=-1), (-1, self.dim) + ) + values = np.zeros(len(coords), dtype=np.float64) + with tqdm.trange(len(coords)) as iterator: + iterator.set_description("Contour Plot Scan") + for i in iterator: + # TODO: Incorporate state vector and density matrix modes for higher speed + values[i] = self.metric.from_circuit( + circuit_descriptor=self.solver.circuit, + parameters=coords[i] @ self.axes + origin, + mode="samples", + ) + return values, coords + + def plot( + self, mode: str = "surface", points: int = 25, distance: float = np.pi + ) -> pg.Figure: + """Plots the loss landscape + The surface plot is the best 3D visualization, but it uses the plotly dynamic interface, + it also has an overhead contour. For simple 2D plots which can be used as matplotlib + graphics or easily used in publications, use line and contour modes. + + :type mode: str + :param mode: line, contour or surface, what type of plot do we want? + :type points: int + :param points: number of points to sample for the metric + :type distance: float + :param distance: the range around the current parameters that we need to sample to + :returns: The figure object that has been generated + :rtype: Plotly or matplotlib figure object + :raises NotImplementedError: For the 1D plotting. TODO Implement 1D plots. + + Increasing the number of points improves the quality of the plot but takes a + lot more time, it scales quadratically in the number of points. Lowering the + distance is a good idea if using fewer points, since you get the same number + of points for a small region. Note that these plots can be deceptive, there might + be large ridges that get missed due to lack of resolution of the points, + always be careful and try to use as many points as possible before making + a final inference. + """ + assert mode in ["line", "contour", "surface"] + if mode == "contour": + assert ( + self.dim == 2 + ), "Contour plots can only be drawn with 2-dimensional axes" + origin = self.solver.model.trainable_variables[0] + data, _coords = self.scan(points, distance, origin) + data = np.reshape(data, (points, points)) + scan_range = np.linspace(-distance, +distance, points) + fig = pg.Figure(data=pg.Contour(z=data, x=scan_range, y=scan_range)) + return fig + elif mode == "surface": + assert ( + self.dim == 2 + ), "Contour plots can only be drawn with 2-dimensional axes" + origin = self.solver.model.trainable_variables[0] + data, _coords = self.scan(points, distance, origin) + data = np.reshape(data, (points, points)) + scan_range = np.linspace(-distance, +distance, points) + fig = pg.Figure(data=pg.Surface(z=data, x=scan_range, y=scan_range)) + return fig + else: + raise NotImplementedError("This plotting mode has not been implemented yet") diff --git a/src/modules/applications/qml/qleet/analyzers/training_path.py b/src/modules/applications/qml/qleet/analyzers/training_path.py new file mode 100644 index 000000000..67c3062f4 --- /dev/null +++ b/src/modules/applications/qml/qleet/analyzers/training_path.py @@ -0,0 +1,169 @@ +# Copyright 2021 The QUARK Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +"""Module responsible to generating plots of the training trajectory. + +The training trajectory is the set of parameter values (projected down to some low +dimensional space) that the model had through the different epochs of it's training +process. This when plotted for one model tells us if the loss was decreasing always, +if the learning rate should be lowered, increased, or what the schedule should look +like, etc. When plotted for more than one model, it let's us know if the paths are +converging or not, giving us a view of how likely is our generated solution optimal. +If many of the models converge to the same path and start mixing, then they likely +are optimal, if not they they are more likely to be just random chance solutions. +""" + +import typing as ty + +import numpy as np +from sklearn.manifold import TSNE +from sklearn.decomposition import PCA +import plotly.express as px +import plotly.graph_objects as pg + +from .loss_landscape import LossLandscapePlotter +from ..interface.metas import MetaLogger +from ..simulators.pqc_trainer import PQCSimulatedTrainer + + +class OptimizationPathPlotter(MetaLogger): + """Class which logs the parameter information and plots it over the iterations of training. + + This will be used to plot the parameter values in 2-D or 3-D, rather a t-SNE or PCA projection + or the parameter values. For getting the loss values for the associated training points as a + part of the plot too, see `LossLandscapePathPlotter`. + + This class conforms to the `MetaLogger` interface and can be used as part of an `AnalyzerList` + when plotting the training properties of a circuit. + """ + + def __init__(self, mode: str = "tSNE"): + """Constructs the Path Plotter object. + + :type mode: str + :param mode: The type of projection we use to show the plots in lower dimensions + """ + super().__init__() + assert mode in [ + "tSNE", + "PCA", + ], "Mode of Dimensionality Reduction is not implemented, use PCA or tSNE." + self.dimensionality_reduction = TSNE if mode == "tSNE" else PCA + + def log(self, solver: PQCSimulatedTrainer, _loss: float) -> None: + """Logs the value of the parameters that the circuit currently has. + The parameter values should be a numpy vector. + + :type solver: PQCSimulatedTrainer + :param solver: The trainer module which has the parameters to be plotted + :type _loss: float + :param _loss: The loss value at that epoch, not used by this class + """ + self.data.append(solver.model.trainable_variables[0].numpy()) + self.runs.append(self.trial) + self.item.append(self.counter) + self.counter += 1 + + def plot(self, large_marker_size=5) -> pg.Figure: + """Plots the 2D parameter projections. + For the entire set of runs, the class has logged the parameter values. + Now it reduces the dimensionality of those parameter vectors using PCA or tSNE + and then plots them on a 2D plane. + + :returns: The figure on which the parameter projections are plotted + :rtype: Plotly figure + """ + raw_params = np.stack(self.data) + final_params = self.dimensionality_reduction(n_components=2).fit_transform( + raw_params + ) + max_number_of_runs = max(self.item) + size_values = [ + large_marker_size if size > max_number_of_runs - 5 else 1 + for size in self.item + ] + fig = px.scatter( + x=final_params[:, 0], + y=final_params[:, 1], + color=self.runs, + size=size_values, + ) + return fig + + +class LossLandscapePathPlotter(MetaLogger): + """An module to plot the training path of the PQC on the loss landscape + + This class is an extension of the Loss Landscape plotter and the Training + Path plotter, puts both the ideas together and shows how the different models + ended us at different parts of the loss landscape. + """ + + def __init__(self, base_plotter: LossLandscapePlotter): + """Constructor for the LossLandscapePathPlotter. + + :type base_plotter: LossLandscapePlotter + :param base_plotter: The loss landscape plotter to plot the training path on top of + """ + super().__init__() + self.loss: ty.List[float] = [] + self.plotter = base_plotter + + def log(self, solver: "PQCSimulatedTrainer", loss: float): + """Logs the value of the parameters that the circuit currently has. + The parameter values should be a numpy vector. + + :type solver: PQCSimulatedTrainer + :param solver: The trainer module which has the parameters to be plotted + :type loss: float + :param loss: The value of the loss at the current epoch + """ + self.data.append( + self.plotter.axes @ solver.model.trainable_variables[0].numpy() + ) + self.loss.append(loss) + self.runs.append(self.trial) + self.item.append(self.counter) + self.counter += 1 + + def plot(self): + """Plots the 2D parameter projections with the loss value on the 3rd dimension. + For the entire set of runs, the class has logged the parameter values. + Now it reduces the dimensionality of those parameter vectors using PCA or tSNE + and then plots them on a 2D plane, associates them with a loss value to put on + the third dimension. This output is coupled with the actual loss landscape drawing + and returned. + + :returns: The figure on which the parameter projections are plotted + :rtype: Plotly figure + """ + + data = np.array(self.data) + loss = np.array(self.loss) + max_number_of_runs = max(self.item) + size_values = np.array( + [12 if size > max_number_of_runs - 5 else 5 for size in self.item] + ) + fig = pg.Figure( + data=[ + pg.Scatter3d( + x=data[:, 0], + y=data[:, 1], + z=-loss, + mode="markers", + marker=dict(color=self.runs, size=size_values), + ) + ] + ) + return fig diff --git a/src/modules/applications/qml/qleet/interface/__init__.py b/src/modules/applications/qml/qleet/interface/__init__.py new file mode 100644 index 000000000..a2e3a41aa --- /dev/null +++ b/src/modules/applications/qml/qleet/interface/__init__.py @@ -0,0 +1,15 @@ +# Copyright 2021 The QUARK Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +"""Module containing qLEET interfaces""" diff --git a/src/modules/applications/qml/qleet/interface/circuit.py b/src/modules/applications/qml/qleet/interface/circuit.py new file mode 100644 index 000000000..d621001e6 --- /dev/null +++ b/src/modules/applications/qml/qleet/interface/circuit.py @@ -0,0 +1,199 @@ +# Copyright 2021 The QUARK Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +"""This module provides the interface to the circuits that the user specifies and library uses. + +This makes the abstractions which ensures all operations in the library are +backend agnostic, by allowing us to get the circuit back in the desired library's +form, qiskit or pytket. It allows the user to specify the circuit in any +form. It also takes the loss function specification as a Pauli String. + +It also exposes functions that the user can use to convert their circuits to a +qiskit backend. +WARNING: the conversion is done through a OpenQASM intermediate, operations not +supported on QASM cannot be converted directly, please provide your circuit in a +Qiskit backend in that case. +""" + +import typing + +import numpy as np +import sympy +import qiskit +import qiskit.quantum_info + + +def convert_to_qiskit( + circuit: typing.Union[qiskit.QuantumCircuit] +) -> qiskit.QuantumCircuit: + """Converts any circuit to qiskit + :type circuit: Circuit in any supported library + :param circuit: input circuit in any framework + :raises ValueError: if the circuit is not from one of the supported frameworks + :return: circuit in qiskit + :rtype: qiskit.QuantumCircuit + """ + + if isinstance(circuit, qiskit.QuantumCircuit): + return circuit + else: + raise ValueError( + f"Expected a circuit object in qiskit, got {type(circuit)}" + ) + + +class CircuitDescriptor: + """The interface for users to provide a circuit in any framework and visualize it in qLEET. + + It consists of 3 parts: + * Circuit: which has the full ansatz preparation from the start where + * Params: list of parameters which are used to parameterize the circuit + * Cost Function: presently a pauli string, which we measure to get the + output we are optimizing over + + Combined they form the full the parameterized quantum circuit from the initial qubits to the end + measurement. + """ + + def __init__( + self, + circuit: typing.Union[qiskit.QuantumCircuit], + params: typing.List[typing.Union[sympy.Symbol, qiskit.circuit.Parameter]], + cost_function: typing.Union[ + qiskit.quantum_info.PauliList, None + ] = None, + ): + """Constructor for the CircuitDescriptor + + :type circuit: Circuit in any supported library + :param circuit: The full circuit which generates the required quantum state + :type params: list[sympy.Symbol] + :param params: The list of parameters to optimize over + :type cost_function: PauliSum in any supported library + :param cost_function: The measurement operation as a PauliString + + If you are not providing the full list of parameters of the circuit because + you don't want to optimize over some of those parameters, because use a + Parameter Resolver to resolve those parameter values before you pass in the + lists. The list of parameters passed in here ought to be complete. + """ + self._circuit = circuit + self._params = params + self._cost = cost_function + + @property + def default_backend(self) -> str: + """Returns the backend in which the user had provided the circuit. + :returns: The name of the default backend + :rtype: str + :raises ValueError: if the given circuit is not from a supported library + """ + if isinstance(self._circuit, qiskit.QuantumCircuit): + return "qiskit" + raise ValueError("Unsupported framework of circuit") + + @classmethod + def from_qasm( + cls, + qasm_str: str, + params: typing.List[typing.Union[sympy.Symbol, qiskit.circuit.Parameter]], + cost_function: typing.Union[ + qiskit.quantum_info.PauliList, None + ], + backend: str = "qiskit", + ): + """Generate the descriptor from OpenQASM string + + :type qasm_str: str + :param qasm_str: OpenQASM string for each part of the circuit + :type params: list[sympy.Symbol] + :param params: list of sympy symbols which act as parameters for the PQC + :type cost_function: PauliSum + :param cost_function: pauli-string operator to implement cost function + :type backend: str + :param backend: backend for the circuit descriptor objects + :return: The CircuitDescriptor object + :rtype: CircuitDescriptor + :raises ValueError: if one of the 3 supported backends is not the input + """ + circuit: typing.Union[qiskit.QuantumCircuit] + if backend == "qiskit": + circuit = qiskit.QuantumCircuit.from_qasm_str(qasm_str) + else: + raise ValueError() + + return CircuitDescriptor( + circuit=circuit, params=params, cost_function=cost_function + ) + + @property + def parameters( + self, + ) -> typing.List[typing.Union[sympy.Symbol, qiskit.circuit.Parameter]]: + """The list of sympy symbols to resolve as parameters, will be swept from 0 to 2*pi + :return: list of parameters + """ + return self._params + + def __len__(self) -> int: + """Number of parameters in the variational circuit + :return: number of parameters in the circuit + """ + return len(self.parameters) + + @property + def qiskit_circuit(self) -> qiskit.QuantumCircuit: + """Get the circuit in qiskit + :return: the qiskit representation of the circuit + :rtype: qiskit.QuantumCircuit + """ + return convert_to_qiskit(self._circuit) + + @property + def num_qubits(self) -> int: + """Get the number of qubits for a circuit + :return: the number of qubits in the circuit + :rtype: int + :raises ValueError: if unsupported circuit framework is given + """ + if isinstance(self._circuit, qiskit.QuantumCircuit): + return self._circuit.num_qubits + else: + raise ValueError("Unsupported framework of circuit") + + def __eq__(self, other: typing.Any) -> bool: + """Checks equality between a CircuitDescriptor and another object""" + if isinstance(other, CircuitDescriptor): + return ( + np.array_equal(self.parameters, other.parameters) + and self.qiskit_circuit == other.qiskit_circuit + ) + return False + + def __repr__(self) -> str: + """Prints the representation of the CircuitDescriptor + You can eval this to get the object back. + + :returns: The repr string + :rtype: str + """ + return f"qleet.CircuitDescriptor({repr(self._circuit)}, {repr(self._params)})" + + def __str__(self) -> str: + """Prints the string form of the CircuitDescriptor + + :returns: The string form + :rtype: str + """ + return f"qleet.CircuitDescriptor({repr(self._circuit)})" diff --git a/src/modules/applications/qml/qleet/interface/metas.py b/src/modules/applications/qml/qleet/interface/metas.py new file mode 100644 index 000000000..2b877f9f4 --- /dev/null +++ b/src/modules/applications/qml/qleet/interface/metas.py @@ -0,0 +1,130 @@ +# Copyright 2021 The QUARK Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +"""This module houses the interfaces for analyzers, and provide a utility container AnalyzerList + +* MetaLogger is interface for those analyzers which need the state of the + circuit at each timestep in training. +* MetaExplorer is the interaface for those analyzers which can generate + properties from a single snapshot of the circuit. +* AnalyzerList is the convinience container which acts as a list of analyzers + which easy to use API. +""" + +import typing +from abc import abstractmethod, ABC + +if typing.TYPE_CHECKING: + from ..simulators.pqc_trainer import PQCSimulatedTrainer + + +class MetaLogger(ABC): + """Abstract class to represent interface of logging. + Logs the present state of the model during training. + """ + + def __init__(self): + """Constructs the Logger object.""" + self.trial, self.counter = 0, 0 + self.data = [] + self.runs = [] + self.item = [] + + @abstractmethod + def log(self, solver: "PQCSimulatedTrainer", loss: float): + """Logs information at one timestep about either the solver or the present loss. + + :type solver: PQCSimulatedTrainer + :param solver: The state of the PQC trainer at the current timestep + :type loss: float + :param loss: The loss at the current timestep + """ + raise NotImplementedError + + @abstractmethod + def plot(self): + """Plots the values logged by the logger.""" + raise NotImplementedError + + def next(self): + """Moves the logger to analyzing the next run or model path.""" + self.trial += 1 + self.counter = 0 + + +class MetaExplorer(ABC): + """Abstract class to represent interface of analyzing a the current state of the circuit. + Treats the parameters of the circuit as a snapshot. + """ + + def __init__(self): + """Constructs the Explorer object.""" + + +class AnalyzerList: + """Container class, Stores a list of loggers. + + All the loggers can be asked to log the information they need together. + The information to be logged can be provided to the Analyzer List in one convinient + function call, and all the associated functions for all the loggers get called + which can accept that form of data. All the loggers can also together be moved to + the next model. + """ + + def __init__(self, *args: typing.Union[MetaLogger, MetaExplorer]): + """Constructor for the Analyzer List + Takes the list of MetaLoggers and MetaExplorers as input. + """ + self._analyzers: typing.Tuple[ + typing.Union[MetaLogger, MetaExplorer] + ] = typing.cast(typing.Tuple[typing.Union[MetaLogger, MetaExplorer]], args) + + def __str__(self) -> str: + return "\n".join([str(analyzer) for analyzer in self._analyzers]) + + def log(self, solver: "PQCSimulatedTrainer", loss: float) -> None: + """Logs the current state of model in all the loggers. + Does not ask the `MetaAnalyzers` to log the information since they don't + implement the logging interface. + :type solver: PQCSimulatedTrainer + :param solver: The PQC trainer whose parameters are to be logged + :type loss: float + :param loss: Loss value on the current epoch + """ + for analyzer in self._analyzers: + if isinstance(analyzer, MetaLogger): + analyzer.log(solver, loss) + + def next(self) -> None: + """Moves the loggers to logging of the next model. + Completes the logging for the current training path.""" + for analyzer in self._analyzers: + if isinstance(analyzer, MetaLogger): + analyzer.next() + + def __getitem__(self, item: int) -> typing.Union[MetaLogger, MetaExplorer]: + """Returns the given Logger or Explorer + :type item: int + :param item: The index of the analyzer we want + :returns: The analyzer at the given position + :rtype: `MetaLogger` or `MetaExplorer` + """ + return self._analyzers[item] + + def __iter__(self) -> typing.Iterable[typing.Union[MetaLogger, MetaExplorer]]: + """Allows iteration over all the Loggers or Explorers in the List + :retuns: List of all the Analyzers in the `AnalyzerList`. + :rtype: `MetaLogger` or `MetaExplorer` + """ + return iter(self._analyzers) diff --git a/src/modules/applications/qml/qleet/simulators/__init__.py b/src/modules/applications/qml/qleet/simulators/__init__.py new file mode 100644 index 000000000..4b7bd0e0f --- /dev/null +++ b/src/modules/applications/qml/qleet/simulators/__init__.py @@ -0,0 +1,15 @@ +# Copyright 2021 The QUARK Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +"""Module containing qLEET simulators""" diff --git a/src/modules/applications/qml/qleet/simulators/circuit_simulators.py b/src/modules/applications/qml/qleet/simulators/circuit_simulators.py new file mode 100644 index 000000000..e8a836232 --- /dev/null +++ b/src/modules/applications/qml/qleet/simulators/circuit_simulators.py @@ -0,0 +1,123 @@ +# Copyright 2021 The QUARK Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +"""Module to draw samples from the circuit. +Used for computing properties of the circuit like Entanglability and Expressibility. +""" + +import typing + +import numpy as np +import qiskit + +from qiskit_aer.noise import NoiseModel as qiskitNoiseModel +from ..interface.circuit import CircuitDescriptor +from qiskit import transpile +from qiskit_aer import AerSimulator + + +class CircuitSimulator: + """The interface for users to execute their CircuitDescriptor objects""" + + def __init__( + self, + circuit: CircuitDescriptor, + noise_model: typing.Union[ + qiskitNoiseModel, None + ] = None, + ) -> None: + """Initialize the state simulator + :type circuit: CircuitDescriptor + :param circuit: the target circuit to simulate + :type noise_model: Noise model as a dict or in the library format + :param noise_model: the noise model as dict or empty dict for density matrix simulations, + None if performing state vector simulations + """ + self.circuit = circuit + self.noise_model = noise_model + self._result = None + + @property + def result( + self, + ) -> typing.Optional[np.ndarray]: + """Get the results stored from the circuit simulator + :return: stored result of the circuit simulation if it has been performed, else None. + :rtype: np.array or None + """ + return self._result + + def simulate( + self, + param_resolver: typing.Dict[qiskit.circuit.Parameter, float], + shots: int = 1024, + ) -> np.ndarray: + """Simulate to get the state vector or the density matrix + :type param_resolver: Dict to resolve all parameters to a static float value + :param param_resolver: a dictionary of all the symbols/parameters mapping to their values + :type shots: int + :param shots: number of times to run the qiskit density matrix simulator + :returns: state vector or density matrix resulting from the simulation + :rtype: np.array + :raises NotImplementedError: if circuit simulation is not supported for a backend + """ + + if self.circuit.default_backend == "qiskit": + backend = AerSimulator() # .get_backend('aer_simulator_statevector') + + # Use assign_parameters instead of bind_parameters + circuit = self.circuit.qiskit_circuit.assign_parameters(param_resolver) + + # Check if a noise model is provided + if self.noise_model is not None: + # Add a snapshot for density matrix + circuit.save_statevector(label='statevector') + + # Transpile the circuit for the backend + new_circuit = transpile(circuit, backend) + + # Execute the circuit with noise model + job = backend.run( + new_circuit, + shots=shots, + noise_model=self.noise_model, + backend_options={ + "method": "density_matrix"}) + + # Get the result + result = job.result() + + result_data = result.data(0)["statevector"] + else: + # Add a snapshot for statevector + circuit.save_statevector(label='statevector') + + # Transpile the circuit for the backend + new_circuit = transpile(circuit, backend) + + # Execute the circuit without noise + job = backend.run(new_circuit, shots=shots) + + # Get the result + result = job.result() + + result_data = result.data(0)["statevector"] + + else: + raise NotImplementedError( + "Parametrized circuit simulation is not implemented for this backend." + ) + + self._result = result_data + return result_data diff --git a/src/modules/applications/qml/QML.py b/src/modules/applications/qml/qml.py similarity index 95% rename from src/modules/applications/qml/QML.py rename to src/modules/applications/qml/qml.py index 91f34fc5b..5720fccea 100644 --- a/src/modules/applications/qml/QML.py +++ b/src/modules/applications/qml/qml.py @@ -14,7 +14,7 @@ from abc import ABC, abstractmethod -from modules.applications.Application import Application +from modules.applications.application import Application class QML(Application, ABC): diff --git a/src/modules/applications/qml/Training.py b/src/modules/applications/qml/training.py similarity index 100% rename from src/modules/applications/qml/Training.py rename to src/modules/applications/qml/training.py diff --git a/src/modules/Core.py b/src/modules/core.py similarity index 97% rename from src/modules/Core.py rename to src/modules/core.py index 043849b8a..dee2c7313 100644 --- a/src/modules/Core.py +++ b/src/modules/core.py @@ -12,16 +12,15 @@ # See the License for the specific language governing permissions and # limitations under the License. -from __future__ import \ - annotations # Needed if you want to type hint a method with the type of the enclosing class +from __future__ import annotations # Needed if you want to type hint a method with the type of the enclosing class import os import sys from abc import ABC, abstractmethod from typing import final -from Metrics import Metrics from utils import _get_instance_with_sub_options +from metrics import Metrics class Core(ABC): diff --git a/src/modules/devices/braket/Braket.py b/src/modules/devices/braket/braket.py similarity index 99% rename from src/modules/devices/braket/Braket.py rename to src/modules/devices/braket/braket.py index b70c8bd21..00f755b9a 100644 --- a/src/modules/devices/braket/Braket.py +++ b/src/modules/devices/braket/braket.py @@ -23,7 +23,7 @@ from botocore.exceptions import ProfileNotFound from braket.aws import AwsSession -from modules.devices.Device import Device +from modules.devices.device import Device class Braket(Device, ABC): diff --git a/src/modules/devices/braket/Ionq.py b/src/modules/devices/braket/ionq.py similarity index 97% rename from src/modules/devices/braket/Ionq.py rename to src/modules/devices/braket/ionq.py index 6cbf36b51..66ba90f57 100644 --- a/src/modules/devices/braket/Ionq.py +++ b/src/modules/devices/braket/ionq.py @@ -16,7 +16,7 @@ from braket.aws import AwsDevice -from modules.devices.braket.Braket import Braket +from modules.devices.braket.braket import Braket class Ionq(Braket): diff --git a/src/modules/devices/braket/LocalSimulator.py b/src/modules/devices/braket/local_simulator.py similarity index 97% rename from src/modules/devices/braket/LocalSimulator.py rename to src/modules/devices/braket/local_simulator.py index df82288d2..0590f8028 100644 --- a/src/modules/devices/braket/LocalSimulator.py +++ b/src/modules/devices/braket/local_simulator.py @@ -14,7 +14,7 @@ from braket.devices import LocalSimulator as LocalSimulatorBraket -from modules.devices.braket.Braket import Braket +from modules.devices.braket.braket import Braket class LocalSimulator(Braket): diff --git a/src/modules/devices/braket/OQC.py b/src/modules/devices/braket/oqc.py similarity index 97% rename from src/modules/devices/braket/OQC.py rename to src/modules/devices/braket/oqc.py index eeb2c5780..2957a34b0 100644 --- a/src/modules/devices/braket/OQC.py +++ b/src/modules/devices/braket/oqc.py @@ -16,7 +16,7 @@ from braket.aws import AwsDevice -from modules.devices.braket.Braket import Braket +from modules.devices.braket.braket import Braket class OQC(Braket): diff --git a/src/modules/devices/braket/Rigetti.py b/src/modules/devices/braket/rigetti.py similarity index 97% rename from src/modules/devices/braket/Rigetti.py rename to src/modules/devices/braket/rigetti.py index ef998de31..1d02e360b 100644 --- a/src/modules/devices/braket/Rigetti.py +++ b/src/modules/devices/braket/rigetti.py @@ -16,7 +16,7 @@ from braket.aws import AwsDevice -from modules.devices.braket.Braket import Braket +from modules.devices.braket.braket import Braket class Rigetti(Braket): diff --git a/src/modules/devices/braket/SV1.py b/src/modules/devices/braket/sv1.py similarity index 97% rename from src/modules/devices/braket/SV1.py rename to src/modules/devices/braket/sv1.py index 941ff15b6..70e455425 100644 --- a/src/modules/devices/braket/SV1.py +++ b/src/modules/devices/braket/sv1.py @@ -16,7 +16,7 @@ from braket.aws import AwsDevice -from modules.devices.braket.Braket import Braket +from modules.devices.braket.braket import Braket class SV1(Braket): diff --git a/src/modules/devices/braket/TN1.py b/src/modules/devices/braket/tn1.py similarity index 97% rename from src/modules/devices/braket/TN1.py rename to src/modules/devices/braket/tn1.py index 4d26262b0..cad2dbb4a 100644 --- a/src/modules/devices/braket/TN1.py +++ b/src/modules/devices/braket/tn1.py @@ -16,7 +16,7 @@ from braket.aws import AwsDevice -from modules.devices.braket.Braket import Braket +from modules.devices.braket.braket import Braket class TN1(Braket): diff --git a/src/modules/devices/Device.py b/src/modules/devices/device.py similarity index 97% rename from src/modules/devices/Device.py rename to src/modules/devices/device.py index bde068e9c..4b9c402f1 100644 --- a/src/modules/devices/Device.py +++ b/src/modules/devices/device.py @@ -13,9 +13,8 @@ # limitations under the License. from abc import ABC - -from modules.Core import Core -from utils import end_time_measurement, start_time_measurement +from modules.core import Core +from utils import start_time_measurement, end_time_measurement class Device(Core, ABC): diff --git a/src/modules/devices/HelperClass.py b/src/modules/devices/helper_class.py similarity index 97% rename from src/modules/devices/HelperClass.py rename to src/modules/devices/helper_class.py index a1d1e0519..cb70669cd 100644 --- a/src/modules/devices/HelperClass.py +++ b/src/modules/devices/helper_class.py @@ -12,7 +12,7 @@ # See the License for the specific language governing permissions and # limitations under the License. -from modules.devices.Device import Device +from modules.devices.device import Device class HelperClass(Device): diff --git a/src/modules/devices/Local.py b/src/modules/devices/local.py similarity index 97% rename from src/modules/devices/Local.py rename to src/modules/devices/local.py index 65ee88ded..bc04a427e 100644 --- a/src/modules/devices/Local.py +++ b/src/modules/devices/local.py @@ -12,7 +12,7 @@ # See the License for the specific language governing permissions and # limitations under the License. -from modules.devices.Device import Device +from modules.devices.device import Device class Local(Device): diff --git a/src/modules/devices/pulser/MockNeutralAtomDevice.py b/src/modules/devices/pulser/mock_neutral_atom_device.py similarity index 98% rename from src/modules/devices/pulser/MockNeutralAtomDevice.py rename to src/modules/devices/pulser/mock_neutral_atom_device.py index 3e6049c40..f43fa5af1 100644 --- a/src/modules/devices/pulser/MockNeutralAtomDevice.py +++ b/src/modules/devices/pulser/mock_neutral_atom_device.py @@ -20,7 +20,7 @@ from pulser.noise_model import NoiseModel from pulser_simulation import QutipBackend -from modules.devices.pulser.Pulser import Pulser +from modules.devices.pulser.pulser import Pulser class MockNeutralAtomDevice(Pulser): diff --git a/src/modules/devices/pulser/Pulser.py b/src/modules/devices/pulser/pulser.py similarity index 97% rename from src/modules/devices/pulser/Pulser.py rename to src/modules/devices/pulser/pulser.py index 11064cfc3..0cae17aa6 100644 --- a/src/modules/devices/pulser/Pulser.py +++ b/src/modules/devices/pulser/pulser.py @@ -14,7 +14,7 @@ from abc import ABC, abstractmethod -from modules.devices.Device import Device +from modules.devices.device import Device class Pulser(Device, ABC): diff --git a/src/modules/devices/qrisp_simulator/QrispSimulator.py b/src/modules/devices/qrisp_simulator/qrisp_simulator.py similarity index 97% rename from src/modules/devices/qrisp_simulator/QrispSimulator.py rename to src/modules/devices/qrisp_simulator/qrisp_simulator.py index 18bdfc2b9..e5afed76b 100644 --- a/src/modules/devices/qrisp_simulator/QrispSimulator.py +++ b/src/modules/devices/qrisp_simulator/qrisp_simulator.py @@ -15,8 +15,8 @@ from abc import ABC from typing import TypedDict -from modules.Core import Core -from modules.devices.Device import Device +from modules.core import Core +from modules.devices.device import Device class QrispSimulator(Device, ABC): diff --git a/src/modules/devices/quantum_annealing_sampler.py b/src/modules/devices/quantum_annealing_sampler.py new file mode 100644 index 000000000..8e79ff106 --- /dev/null +++ b/src/modules/devices/quantum_annealing_sampler.py @@ -0,0 +1,62 @@ +# Copyright 2021 The QUARK Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from dwave.system import DWaveSampler, AutoEmbeddingComposite + +from modules.devices.device import Device +from modules.core import Core + + +class QuantumAnnealingSampler(Device): + """ + Class for D-Wave quantum annealer. + """ + + def __init__(self): + """ + Constructor method + """ + super().__init__(device_name="quantum annealer dwave") + + dwave_token = input("What is your Dwave-Solver-API-Token? \n Copy-paste it here from your DWave-Leap account: ") + self.device = AutoEmbeddingComposite(DWaveSampler(token=dwave_token)) + self.submodule_options = [] + + @staticmethod + def get_requirements() -> list[dict]: + """ + Return requirements of this module. + + :return: List of dict with requirements of this module + """ + return [{"name": "dwave-system", "version": "1.23.0"}] + + def get_parameter_options(self) -> dict: + """ + Returns the configurable settings for this mapping. + + :return: An empty dict + """ + return { + + } + + def get_default_submodule(self, option: str) -> Core: + """ + This module has no submodules. + + :param option: Submodule option (not applicable) + :raises ValueError: Always raised because this module has no submodules + """ + raise ValueError("This module has no submodules.") diff --git a/src/modules/devices/SimulatedAnnealingSampler.py b/src/modules/devices/simulated_annealing_sampler.py similarity index 97% rename from src/modules/devices/SimulatedAnnealingSampler.py rename to src/modules/devices/simulated_annealing_sampler.py index b51aab3e9..d73a81f1b 100644 --- a/src/modules/devices/SimulatedAnnealingSampler.py +++ b/src/modules/devices/simulated_annealing_sampler.py @@ -14,7 +14,7 @@ import dwave.samplers -from modules.devices.Device import Device +from modules.devices.device import Device class SimulatedAnnealingSampler(Device): diff --git a/src/modules/solvers/Annealer.py b/src/modules/solvers/annealer.py similarity index 92% rename from src/modules/solvers/Annealer.py rename to src/modules/solvers/annealer.py index 548dbff28..c53314f03 100644 --- a/src/modules/solvers/Annealer.py +++ b/src/modules/solvers/annealer.py @@ -12,12 +12,12 @@ # See the License for the specific language governing permissions and # limitations under the License. -import logging from typing import TypedDict +import logging -from modules.Core import Core -from modules.solvers.Solver import Solver -from utils import end_time_measurement, start_time_measurement +from modules.solvers.solver import Solver +from modules.core import Core +from utils import start_time_measurement, end_time_measurement class Annealer(Solver): @@ -40,8 +40,7 @@ def get_default_submodule(self, option: str) -> Core: :return: Instance of the default submodule """ if option == "Simulated Annealer": - from modules.devices.SimulatedAnnealingSampler import \ - SimulatedAnnealingSampler # pylint: disable=C0415 + from modules.devices.simulated_annealing_sampler import SimulatedAnnealingSampler # pylint: disable=C0415 return SimulatedAnnealingSampler() else: raise NotImplementedError(f"Device Option {option} not implemented") diff --git a/src/modules/solvers/ClassicalSAT.py b/src/modules/solvers/classical_sat.py similarity index 93% rename from src/modules/solvers/ClassicalSAT.py rename to src/modules/solvers/classical_sat.py index 1b24087f4..5afd345d2 100644 --- a/src/modules/solvers/ClassicalSAT.py +++ b/src/modules/solvers/classical_sat.py @@ -12,15 +12,15 @@ # See the License for the specific language governing permissions and # limitations under the License. -import logging from typing import TypedDict +import logging from pysat.examples.rc2 import RC2 from pysat.formula import WCNF -from modules.Core import Core -from modules.solvers.Solver import Solver -from utils import end_time_measurement, start_time_measurement +from modules.solvers.solver import Solver +from modules.core import Core +from utils import start_time_measurement, end_time_measurement class ClassicalSAT(Solver): @@ -52,7 +52,7 @@ def get_default_submodule(self, option: str) -> Core: :return: Instance of the default submodule """ if option == "Local": - from modules.devices.Local import Local # pylint: disable=C0415 + from modules.devices.local import Local # pylint: disable=C0415 return Local() else: raise NotImplementedError(f"Device Option {option} not implemented") diff --git a/src/modules/solvers/GreedyClassicalPVC.py b/src/modules/solvers/greedy_classical_pvc.py similarity index 95% rename from src/modules/solvers/GreedyClassicalPVC.py rename to src/modules/solvers/greedy_classical_pvc.py index 00cd4fd5a..7d6482b2b 100644 --- a/src/modules/solvers/GreedyClassicalPVC.py +++ b/src/modules/solvers/greedy_classical_pvc.py @@ -16,9 +16,9 @@ import networkx as nx -from modules.Core import Core -from modules.solvers.Solver import Solver -from utils import end_time_measurement, start_time_measurement +from modules.solvers.solver import Solver +from modules.core import Core +from utils import start_time_measurement, end_time_measurement class GreedyClassicalPVC(Solver): @@ -50,7 +50,7 @@ def get_default_submodule(self, option: str) -> Core: :return: Instance of the default submodule """ if option == "Local": - from modules.devices.Local import Local # pylint: disable=C0415 + from modules.devices.local import Local # pylint: disable=C0415 return Local() else: raise NotImplementedError(f"Device Option {option} not implemented") diff --git a/src/modules/solvers/GreedyClassicalTSP.py b/src/modules/solvers/greedy_classical_tsp.py similarity index 93% rename from src/modules/solvers/GreedyClassicalTSP.py rename to src/modules/solvers/greedy_classical_tsp.py index 81f2cdbd1..bca0f0243 100644 --- a/src/modules/solvers/GreedyClassicalTSP.py +++ b/src/modules/solvers/greedy_classical_tsp.py @@ -17,9 +17,9 @@ import networkx as nx from networkx.algorithms import approximation as approx -from modules.Core import Core -from modules.solvers.Solver import Solver -from utils import end_time_measurement, start_time_measurement +from modules.solvers.solver import Solver +from modules.core import Core +from utils import start_time_measurement, end_time_measurement class GreedyClassicalTSP(Solver): @@ -51,7 +51,7 @@ def get_default_submodule(self, option: str) -> Core: :return: Instance of the default submodule """ if option == "Local": - from modules.devices.Local import Local # pylint: disable=C0415 + from modules.devices.local import Local # pylint: disable=C0415 return Local() else: raise NotImplementedError(f"Device Option {option} not implemented") diff --git a/src/modules/solvers/MIPsolverACL.py b/src/modules/solvers/mip_solver_acl.py similarity index 95% rename from src/modules/solvers/MIPsolverACL.py rename to src/modules/solvers/mip_solver_acl.py index 63aa321cc..6ca30106b 100644 --- a/src/modules/solvers/MIPsolverACL.py +++ b/src/modules/solvers/mip_solver_acl.py @@ -28,12 +28,11 @@ # in all copies or substantial portions of the Software. from typing import TypedDict - import pulp -from modules.Core import Core -from modules.solvers.Solver import Solver -from utils import end_time_measurement, start_time_measurement +from modules.solvers.solver import Solver +from modules.core import Core +from utils import start_time_measurement, end_time_measurement class MIPaclp(Solver): @@ -65,7 +64,7 @@ def get_default_submodule(self, option: str) -> Core: :return: Instance of the default submodule """ if option == "Local": - from modules.devices.Local import Local # pylint: disable=C0415 + from modules.devices.local import Local # pylint: disable=C0415 return Local() else: raise NotImplementedError(f"Device Option {option} not implemented") diff --git a/src/modules/solvers/mip_solver_bp.py b/src/modules/solvers/mip_solver_bp.py new file mode 100644 index 000000000..45e80fb09 --- /dev/null +++ b/src/modules/solvers/mip_solver_bp.py @@ -0,0 +1,161 @@ +# Copyright 2021 The QUARK Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +import logging +from pathlib import Path +from typing import TypedDict +import pyscipopt as scip_opt + +from modules.solvers.solver import * +from utils import start_time_measurement, end_time_measurement + + +class MIPSolver(Solver): + """ + QAOA with some parts copied/derived from https://github.com/aws/amazon-braket-examples. + """ + + def __init__(self): + """ + Constructor method + """ + super().__init__() + self.submodule_options = ["Local"] + + @staticmethod + def get_requirements() -> list[dict]: + """ + Return requirements of this module. + + :return: List of dict with requirements of this module + """ + return [{"name": "pyscipopt", "version": "5.0.1"}] + + def get_default_submodule(self, option: str) -> Core: + """ + Returns the default submodule based on the provided option. + + :param option: Option specifying the submodule + :return: Instance of the corresponding submodule + :raises NotImplementedError: If the option is not recognized + """ + if option == "Local": + from modules.devices.local import Local # pylint: disable=C0415 + return Local() + else: + raise NotImplementedError(f"Device Option {option} not implemented") + + def get_parameter_options(self) -> dict: + """ + Returns the configurable settings for this solver. + + :return: + .. code-block:: python + + return { + "mip_gap": { # number measurements to make on circuit + "values": [0], #default value 0 means optimal solution is required + "description": "What MIP-Gap do you allow?" + }, + "solution_method": { + "values": [-1], # for gurobi: -1=default automatic, 0=primal simplex, + 1=dual simplex, 2=barrier, 3=concurrent, 4=deterministic concurrent, + 5=deterministic concurrent simplex + "description": "Which optimization method do you want?" + }, + "time_limit": { + "values": [60*60*2], # default value: 2 hours + "description": "How much time may the solving take?" + } + } + """ + return { + "mip_gap": { # Number measurements to make on circuit + "values": [0], # Default value 0 means optimal solution is required + "custom_input": True, + "postproc": float, + "description": "What relative gap to the global optimum do you allow (e.g., '0.01' means solutions " + "within one percent of the global optimum are accepted and the optimization terminates)?" + }, + "time_limit": { + "values": [60 * 60 * 2], # Default value: 2 hours + "custom_input": True, + "postproc": int, + "description": "How much time may the solving take (in seconds)?" + } + } + + class Config(TypedDict): + """ + Attributes of a valid config. + + .. code-block:: python + + shots: int + opt_method: str + depth: int + + """ + mip_gap: int + time_limit: int + + def run(self, mapped_problem: any, device_wrapper: any, config: Config, **kwargs: dict) -> tuple[dict, float, dict]: + """ + Run MIP-solver on an optimization problem. + + :param mapped_problem: Optimization problem + :param device_wrapper: Instance of device + :param config: Configuration parameters for the solver + :param kwargs: No additionally settings needed + :return: Tuple consisting of solution and the time it took to compute it and additional solution information + """ + start = start_time_measurement() + + # Save mapped problem to result folder via lp + export_path = kwargs['store_dir'] + mapped_problem.export_as_lp(basename="MIP", path=export_path) + + # Read the lp-file to get the model into a SCIP_OPT-model + scip_model = scip_opt.Model() + scip_model.readProblem(filename=Path(export_path) / Path("MIP.lp")) + + # Config scip solver + scip_model.setParam("limits/gap", config["mip_gap"]) + scip_model.setParam("limits/time", config["time_limit"]) + + # Start the optimization + scip_model.optimize() + + # Get the optimization results + if scip_model.getStatus() == 'infeasible': + logging.warning('The problem is infeasible.') + additional_solution_info = {'obj_value': None, + 'opt_status': 'infeasible'} + return {}, end_time_measurement(), additional_solution_info + else: + if scip_model.getSols() == []: + logging.warning('No solution found within time limit') + additional_solution_info = {'obj_value': None, + 'opt_status': 'no solution found within time limit'} + return {}, end_time_measurement(), additional_solution_info + else: + obj_value = scip_model.getObjVal() + solution = scip_model.getBestSol() + solution_dict = {} + for var in scip_model.getVars(): + var_name = var.__repr__() + var_value = solution[var] + solution_dict[var_name] = var_value + additional_solution_info = {'obj_value': obj_value, + 'opt_status': 'optimal solution'} + return solution_dict, end_time_measurement(start), additional_solution_info diff --git a/src/modules/solvers/NeutralAtomMIS.py b/src/modules/solvers/neutral_atom_mis.py similarity index 96% rename from src/modules/solvers/NeutralAtomMIS.py rename to src/modules/solvers/neutral_atom_mis.py index fe5af9979..ddfc46c6d 100644 --- a/src/modules/solvers/NeutralAtomMIS.py +++ b/src/modules/solvers/neutral_atom_mis.py @@ -12,15 +12,15 @@ # See the License for the specific language governing permissions and # limitations under the License. -import logging from typing import TypedDict +import logging import numpy as np import pulser -from modules.Core import Core -from modules.solvers.Solver import Solver -from utils import end_time_measurement, start_time_measurement +from modules.solvers.solver import Solver +from modules.core import Core +from utils import start_time_measurement, end_time_measurement class NeutralAtomMIS(Solver): @@ -52,8 +52,7 @@ def get_default_submodule(self, option: str) -> Core: :return: Instance of the default submodule """ if option == "MockNeutralAtomDevice": - from modules.devices.pulser.MockNeutralAtomDevice import \ - MockNeutralAtomDevice # pylint: disable=C0415 + from modules.devices.pulser.mock_neutral_atom_device import MockNeutralAtomDevice # pylint: disable=C0415 return MockNeutralAtomDevice() else: raise NotImplementedError(f"Device Option {option} not implemented") diff --git a/src/modules/solvers/PennylaneQAOA.py b/src/modules/solvers/pennylane_qaoa.py similarity index 93% rename from src/modules/solvers/PennylaneQAOA.py rename to src/modules/solvers/pennylane_qaoa.py index 6df779834..3eb655bc7 100644 --- a/src/modules/solvers/PennylaneQAOA.py +++ b/src/modules/solvers/pennylane_qaoa.py @@ -28,9 +28,9 @@ import pennylane as qml from pennylane import numpy as npqml -from modules.Core import Core -from modules.solvers.Solver import Solver -from utils import end_time_measurement, start_time_measurement +from modules.solvers.solver import Solver +from modules.core import Core +from utils import start_time_measurement, end_time_measurement matplotlib.use('Agg') @@ -82,45 +82,37 @@ def get_default_submodule(self, option: str) -> Core: """ if option == "arn:aws:braket:us-east-1::device/qpu/ionq/Harmony": - from modules.devices.braket.Ionq import \ - Ionq # pylint: disable=C0415 + from modules.devices.braket.ionq import Ionq # pylint: disable=C0415 return Ionq("ionq", "arn:aws:braket:us-east-1::device/qpu/ionq/Harmony") elif option == "arn:aws:braket:::device/quantum-simulator/amazon/sv1": - from modules.devices.braket.SV1 import SV1 # pylint: disable=C0415 + from modules.devices.braket.sv1 import SV1 # pylint: disable=C0415 return SV1("SV1", "arn:aws:braket:::device/quantum-simulator/amazon/sv1") elif option == "arn:aws:braket:::device/quantum-simulator/amazon/tn1": - from modules.devices.braket.TN1 import TN1 # pylint: disable=C0415 + from modules.devices.braket.tn1 import TN1 # pylint: disable=C0415 return TN1("TN1", "arn:aws:braket:::device/quantum-simulator/amazon/tn1") elif option == "arn:aws:braket:us-west-1::device/qpu/rigetti/Aspen-M-3": - from modules.devices.braket.Rigetti import \ - Rigetti # pylint: disable=C0415 + from modules.devices.braket.rigetti import Rigetti # pylint: disable=C0415 return Rigetti("Rigetti", "arn:aws:braket:us-west-1::device/qpu/rigetti/Aspen-M-3") elif option == "arn:aws:braket:eu-west-2::device/qpu/oqc/Lucy": - from modules.devices.braket.OQC import OQC # pylint: disable=C0415 + from modules.devices.braket.oqc import OQC # pylint: disable=C0415 return OQC("OQC", "arn:aws:braket:eu-west-2::device/qpu/oqc/Lucy") elif option == "braket.local.qubit": - from modules.devices.HelperClass import \ - HelperClass # pylint: disable=C0415 + from modules.devices.helper_class import HelperClass # pylint: disable=C0415 return HelperClass("braket.local.qubit") elif option == "default.qubit": - from modules.devices.HelperClass import \ - HelperClass # pylint: disable=C0415 + from modules.devices.helper_class import HelperClass # pylint: disable=C0415 return HelperClass("default.qubit") elif option == "default.qubit.autograd": - from modules.devices.HelperClass import \ - HelperClass # pylint: disable=C0415 + from modules.devices.helper_class import HelperClass # pylint: disable=C0415 return HelperClass("default.qubit.autograd") elif option == "qulacs.simulator": - from modules.devices.HelperClass import \ - HelperClass # pylint: disable=C0415 + from modules.devices.helper_class import HelperClass # pylint: disable=C0415 return HelperClass("qulacs.simulator") elif option == "lightning.gpu": - from modules.devices.HelperClass import \ - HelperClass # pylint: disable=C0415 + from modules.devices.helper_class import HelperClass # pylint: disable=C0415 return HelperClass("lightning.gpu") elif option == "lightning.qubit": - from modules.devices.HelperClass import \ - HelperClass # pylint: disable=C0415 + from modules.devices.helper_class import HelperClass # pylint: disable=C0415 return HelperClass("lightning.qubit") else: raise NotImplementedError(f"Device Option {option} not implemented") @@ -478,8 +470,7 @@ def _pseudo_decor(fun, device): @wraps(fun) def ret_fun(*args, **kwargs): # Pre function execution here - from time import \ - time # pylint: disable=W0621 disable=C0415 disable=W0404 + from time import time # pylint: disable=W0621 disable=C0415 disable=W0404 start_timing = time() * 1000 returned_value = fun(*args, **kwargs) # Post execution here diff --git a/src/modules/solvers/QAOA.py b/src/modules/solvers/qaoa.py similarity index 97% rename from src/modules/solvers/QAOA.py rename to src/modules/solvers/qaoa.py index db2c03666..f447b812d 100644 --- a/src/modules/solvers/QAOA.py +++ b/src/modules/solvers/qaoa.py @@ -12,18 +12,18 @@ # See the License for the specific language governing permissions and # limitations under the License. -import logging from time import sleep from typing import TypedDict +import logging import numpy as np -from braket.aws import AwsDevice from braket.circuits import Circuit +from braket.aws import AwsDevice from scipy.optimize import minimize -from modules.Core import Core -from modules.solvers.Solver import Solver -from utils import end_time_measurement, start_time_measurement +from modules.solvers.solver import Solver +from modules.core import Core +from utils import start_time_measurement, end_time_measurement class QAOA(Solver): @@ -65,19 +65,19 @@ def get_default_submodule(self, option: str) -> Core: """ if option == "arn:aws:braket:us-east-1::device/qpu/ionq/Harmony": - from modules.devices.braket.Ionq import Ionq # pylint: disable=C0415 + from modules.devices.braket.ionq import Ionq # pylint: disable=C0415 return Ionq("ionQ", "arn:aws:braket:us-east-1::device/qpu/ionq/Harmony") elif option == "arn:aws:braket:::device/quantum-simulator/amazon/sv1": - from modules.devices.braket.SV1 import SV1 # pylint: disable=C0415 + from modules.devices.braket.sv1 import SV1 # pylint: disable=C0415 return SV1("SV1", "arn:aws:braket:::device/quantum-simulator/amazon/sv1") elif option == "arn:aws:braket:::device/quantum-simulator/amazon/tn1": - from modules.devices.braket.TN1 import TN1 # pylint: disable=C0415 + from modules.devices.braket.tn1 import TN1 # pylint: disable=C0415 return TN1("TN1", "arn:aws:braket:::device/quantum-simulator/amazon/tn1") elif option == "arn:aws:braket:us-west-1::device/qpu/rigetti/Aspen-M-3": - from modules.devices.braket.Rigetti import Rigetti # pylint: disable=C0415 + from modules.devices.braket.rigetti import Rigetti # pylint: disable=C0415 return Rigetti("Rigetti Aspen-9", "arn:aws:braket:us-west-1::device/qpu/rigetti/Aspen-M-3") elif option == "LocalSimulator": - from modules.devices.braket.LocalSimulator import LocalSimulator # pylint: disable=C0415 + from modules.devices.braket.local_simulator import LocalSimulator # pylint: disable=C0415 return LocalSimulator("LocalSimulator") else: raise NotImplementedError(f"Device Option {option} not implemented") diff --git a/src/modules/solvers/QiskitQAOA.py b/src/modules/solvers/qiskit_qaoa.py similarity index 95% rename from src/modules/solvers/QiskitQAOA.py rename to src/modules/solvers/qiskit_qaoa.py index 7aaa86f6e..4000c9162 100644 --- a/src/modules/solvers/QiskitQAOA.py +++ b/src/modules/solvers/qiskit_qaoa.py @@ -16,17 +16,17 @@ from typing import TypedDict import numpy as np + from qiskit.circuit.library import TwoLocal -from qiskit.primitives import Estimator, Sampler +from qiskit.primitives import Sampler, Estimator from qiskit.quantum_info import SparsePauliOp, Statevector -from qiskit_algorithms.minimum_eigensolvers import (QAOA, VQE, - NumPyMinimumEigensolver) -from qiskit_algorithms.optimizers import COBYLA, POWELL, SPSA from qiskit_optimization.applications import OptimizationApplication +from qiskit_algorithms.optimizers import POWELL, SPSA, COBYLA +from qiskit_algorithms.minimum_eigensolvers import VQE, QAOA, NumPyMinimumEigensolver -from modules.Core import Core -from modules.solvers.Solver import Solver -from utils import end_time_measurement, start_time_measurement +from modules.solvers.solver import Solver +from modules.core import Core +from utils import start_time_measurement, end_time_measurement class QiskitQAOA(Solver): @@ -64,8 +64,7 @@ def get_default_submodule(self, option: str) -> Core: :return: Instance of the default submodule """ if option in ["qasm_simulator", "qasm_simulator_gpu"]: - from modules.devices.HelperClass import \ - HelperClass # pylint: disable=C0415 + from modules.devices.helper_class import HelperClass # pylint: disable=C0415 return HelperClass(option) else: raise NotImplementedError(f"Device Option {option} not implemented") diff --git a/src/modules/solvers/QrispQIRO.py b/src/modules/solvers/qrisp_qiro.py similarity index 93% rename from src/modules/solvers/QrispQIRO.py rename to src/modules/solvers/qrisp_qiro.py index 32db711b6..eb04ffeb5 100644 --- a/src/modules/solvers/QrispQIRO.py +++ b/src/modules/solvers/qrisp_qiro.py @@ -16,14 +16,17 @@ from typing import TypedDict from qrisp import QuantumVariable -from qrisp.algorithms.qiro import (QIROProblem, - create_max_indep_cost_operator_reduced, - create_max_indep_replacement_routine, - qiro_init_function, qiro_rx_mixer) +from qrisp.algorithms.qiro import ( + QIROProblem, + create_max_indep_replacement_routine, + create_max_indep_cost_operator_reduced, + qiro_rx_mixer, + qiro_init_function +) from qrisp.qaoa import create_max_indep_set_cl_cost_function -from modules.solvers.Solver import Core, Solver -from utils import end_time_measurement, start_time_measurement +from modules.solvers.solver import Solver, Core +from utils import start_time_measurement, end_time_measurement class QIROSolver(Solver): @@ -56,8 +59,7 @@ def get_default_submodule(self, option: str) -> Core: :raises NotImplemented: If the provided option is not implemented """ if option == "qrisp_simulator": - from modules.devices.qrisp_simulator.QrispSimulator import \ - QrispSimulator # pylint: disable=C0415 + from modules.devices.qrisp_simulator.qrisp_simulator import QrispSimulator # pylint: disable=C0415 return QrispSimulator() # pylint: disable=E1102 else: diff --git a/src/modules/solvers/RandomClassicalPVC.py b/src/modules/solvers/random_classical_pvc.py similarity index 95% rename from src/modules/solvers/RandomClassicalPVC.py rename to src/modules/solvers/random_classical_pvc.py index 5e2bc3b5b..b7d02aa66 100644 --- a/src/modules/solvers/RandomClassicalPVC.py +++ b/src/modules/solvers/random_classical_pvc.py @@ -12,14 +12,13 @@ # See the License for the specific language governing permissions and # limitations under the License. -import random from typing import TypedDict - +import random import networkx as nx -from modules.Core import Core -from modules.solvers.Solver import Solver -from utils import end_time_measurement, start_time_measurement +from modules.solvers.solver import Solver +from modules.core import Core +from utils import start_time_measurement, end_time_measurement class RandomPVC(Solver): @@ -51,7 +50,7 @@ def get_default_submodule(self, option: str) -> Core: :return: Instance of the default submodule """ if option == "Local": - from modules.devices.Local import Local # pylint: disable=C0415 + from modules.devices.local import Local # pylint: disable=C0415 return Local() else: raise NotImplementedError(f"Device Option {option} not implemented") diff --git a/src/modules/solvers/RandomClassicalSAT.py b/src/modules/solvers/random_classical_sat.py similarity index 93% rename from src/modules/solvers/RandomClassicalSAT.py rename to src/modules/solvers/random_classical_sat.py index 4720b3817..6b823efbe 100644 --- a/src/modules/solvers/RandomClassicalSAT.py +++ b/src/modules/solvers/random_classical_sat.py @@ -12,15 +12,16 @@ # See the License for the specific language governing permissions and # limitations under the License. -import logging from typing import TypedDict +import logging import numpy as np + from pysat.formula import WCNF -from modules.Core import Core -from modules.solvers.Solver import Solver -from utils import end_time_measurement, start_time_measurement +from modules.solvers.solver import Solver +from modules.core import Core +from utils import start_time_measurement, end_time_measurement class RandomSAT(Solver): @@ -49,7 +50,7 @@ def get_requirements() -> list[dict]: def get_default_submodule(self, option: str) -> Core: if option == "Local": - from modules.devices.Local import Local # pylint: disable=C0415 + from modules.devices.local import Local # pylint: disable=C0415 return Local() else: raise NotImplementedError(f"Device Option {option} not implemented") diff --git a/src/modules/solvers/RandomClassicalTSP.py b/src/modules/solvers/random_classical_tsp.py similarity index 93% rename from src/modules/solvers/RandomClassicalTSP.py rename to src/modules/solvers/random_classical_tsp.py index 0f3cbcd54..e12cdb89a 100644 --- a/src/modules/solvers/RandomClassicalTSP.py +++ b/src/modules/solvers/random_classical_tsp.py @@ -12,14 +12,13 @@ # See the License for the specific language governing permissions and # limitations under the License. -import random from typing import TypedDict - +import random import networkx as nx -from modules.Core import Core -from modules.solvers.Solver import Solver -from utils import end_time_measurement, start_time_measurement +from modules.solvers.solver import Solver +from modules.core import Core +from utils import start_time_measurement, end_time_measurement class RandomTSP(Solver): @@ -45,7 +44,7 @@ def get_requirements() -> list[dict]: def get_default_submodule(self, option: str) -> Core: if option == "Local": - from modules.devices.Local import Local # pylint: disable=C0415 + from modules.devices.local import Local # pylint: disable=C0415 return Local() else: raise NotImplementedError(f"Device Option {option} not implemented") diff --git a/src/modules/solvers/ReverseGreedyClassicalPVC.py b/src/modules/solvers/reverse_greedy_classical_pvc.py similarity index 95% rename from src/modules/solvers/ReverseGreedyClassicalPVC.py rename to src/modules/solvers/reverse_greedy_classical_pvc.py index e0102dc4b..ab8091b77 100644 --- a/src/modules/solvers/ReverseGreedyClassicalPVC.py +++ b/src/modules/solvers/reverse_greedy_classical_pvc.py @@ -16,9 +16,9 @@ import networkx as nx -from modules.Core import Core -from modules.solvers.Solver import Solver -from utils import end_time_measurement, start_time_measurement +from modules.solvers.solver import Solver +from modules.core import Core +from utils import start_time_measurement, end_time_measurement class ReverseGreedyClassicalPVC(Solver): @@ -41,7 +41,7 @@ def get_default_submodule(self, option: str) -> Core: :return: Instance of the default submodule """ if option == "Local": - from modules.devices.Local import Local # pylint: disable=C0415 + from modules.devices.local import Local # pylint: disable=C0415 return Local() else: raise NotImplementedError(f"Device Option {option} not implemented") diff --git a/src/modules/solvers/ReverseGreedyClassicalTSP.py b/src/modules/solvers/reverse_greedy_classical_tsp.py similarity index 94% rename from src/modules/solvers/ReverseGreedyClassicalTSP.py rename to src/modules/solvers/reverse_greedy_classical_tsp.py index a78fed169..47f57e435 100644 --- a/src/modules/solvers/ReverseGreedyClassicalTSP.py +++ b/src/modules/solvers/reverse_greedy_classical_tsp.py @@ -17,9 +17,9 @@ import networkx as nx from networkx.algorithms import approximation as approx -from modules.Core import Core -from modules.solvers.Solver import Solver -from utils import end_time_measurement, start_time_measurement +from modules.solvers.solver import Solver +from modules.core import Core +from utils import start_time_measurement, end_time_measurement class ReverseGreedyClassicalTSP(Solver): @@ -52,7 +52,7 @@ def get_default_submodule(self, option: str) -> Core: :return: Instance of the default submodule """ if option == "Local": - from modules.devices.Local import Local # pylint: disable=C0415 + from modules.devices.local import Local # pylint: disable=C0415 return Local() else: raise NotImplementedError(f"Device Option {option} not implemented") diff --git a/src/modules/solvers/Solver.py b/src/modules/solvers/solver.py similarity index 98% rename from src/modules/solvers/Solver.py rename to src/modules/solvers/solver.py index 8a72be233..1ae3811e2 100644 --- a/src/modules/solvers/Solver.py +++ b/src/modules/solvers/solver.py @@ -13,8 +13,7 @@ # limitations under the License. from abc import ABC, abstractmethod - -from modules.Core import Core +from modules.core import Core class Solver(Core, ABC): diff --git a/src/Plotter.py b/src/plotter.py similarity index 67% rename from src/Plotter.py rename to src/plotter.py index 228895aad..c638b8bbb 100644 --- a/src/Plotter.py +++ b/src/plotter.py @@ -14,13 +14,13 @@ import logging from collections import defaultdict - -import matplotlib +from matplotlib import pyplot as plt +import numpy as np import pandas as pd import seaborn as sns -from matplotlib import pyplot as plt +import matplotlib +matplotlib.use("Agg") # Use a non-interactive backend for matplotlib -matplotlib.use('Agg') matplotlib.rcParams['savefig.dpi'] = 300 sns.set(style="darkgrid") @@ -96,6 +96,8 @@ def visualize_results(results: list[dict], store_dir: str) -> None: store_dir, required_application_score_keys ) + # Plotter.plot_all_metrics(results, store_dir) # TODO + logging.info("Finished creating plots.") @staticmethod @@ -246,3 +248,117 @@ def _extract_columns(config: dict, rest_result: dict) -> dict: ) return config + + @staticmethod + def make_radar_chart(name, subname, store_dir, stats, attribute_labels): + """ + name: Plot title, + subname: Plot subtitle (relevant experiment info) + store_dir: folder where to store plot, + stats: metric values, + attribute_labels: metric names + """ + + assert len(stats) == len(attribute_labels), "labels and values to plot do not have the same length!" + attribute_labels.append('') + + markers = [0.0, 0.2, 0.4, 0.6, 0.8, 1.0] # can be any length + labels = np.array(attribute_labels) + + angles = np.linspace(0, 2 * np.pi, len(labels) - 1, endpoint=False) + stats = np.concatenate((stats, [stats[0]])) + angles = np.concatenate((angles, [angles[0]])) + + fig, ax = plt.subplots(subplot_kw={'projection': 'polar'}) + + ax.plot(angles, stats, 'o-', linewidth=2) + ax.fill(angles, stats, alpha=0.25) + ax.grid(True) + # ax.grid(c="black") + ax.spines['polar'].set_visible(False) + ax.set_thetagrids(angles[:-1] * 180 / np.pi, labels[:-1]) + ax.set_ylim([0, 1]) + ax.plot(np.linspace(0, 2 * np.pi, 500), np.ones(500), color="k", linewidth=1) + + plt.yticks(markers) + plt.title(subname) + plt.suptitle(name, y=1.02) + + fig.savefig(f"{store_dir}/metrics_collection_test.pdf", dpi=300, + bbox_inches='tight') # dpi=300, bbox_inches='tight' + fig.savefig(f"{store_dir}/metrics_collection_test.png", dpi=300, bbox_inches='tight') + return + + @staticmethod + def plot_all_metrics(results: list[dict], store_dir: str) -> None: + + # Total time + overall_time, overall_time_unit = results[0]["total_time"], results[0]["total_time_unit"] + m_name = results[0]["module"]["module_name"] + + # Use-case specific results + if m_name == "GenerativeModeling": + dataset_name = results[0]["module"]["submodule"]["module_name"] + kl_best = results[0]["module"]["submodule"]["KL_best"] + gen_metrics = results[0]["module"]["submodule"]["generalization_metrics"] # TODO + precision = gen_metrics["precision"] + fidelity = gen_metrics["fidelity"] + + quantum_module = results[0]["module"]["submodule"]["submodule"]["submodule"]["submodule"] + if "population_size" in quantum_module["module_config"]: + pop_size = quantum_module["module_config"]["population_size"] + else: + pop_size = None + max_evaluations = quantum_module["module_config"]["max_evaluations"] + quantum_time, quantum_time_unit = quantum_module["total_time"], quantum_module["total_time_unit"] + if overall_time_unit == quantum_time_unit: + time_ratio = float(quantum_time) / (float(overall_time)) + else: + print('Hybrid module time and overall time have different time units, please check.') + time_ratio = 0. + ent = quantum_module["meyer-wallach"] + expr = quantum_module["expressibility_jsd"] + + metrics_vector = [time_ratio, precision, fidelity, expr, ent] + metrics_names = ['Time ratio', 'Precision', 'Fidelity', 'Expressibility', 'Entanglement'] + plt_title = "GenerativeModeling" + info_str = f"Data: {dataset_name}, Population size: {pop_size}, Max. Evaluations: {max_evaluations}" + + elif m_name == "Classification": + # Results + quantum_module = results[0]["module"]["submodule"]["submodule"] + quantum_time, quantum_time_unit = quantum_module["total_time"], quantum_module["total_time_unit"] + assert quantum_module["module_name"] == "Hybrid", f"Module name is not hybrid but { + quantum_module['module_name']}. Are you sure this is correct?" + if overall_time_unit == quantum_time_unit: + time_ratio = float(quantum_time) / (float(overall_time)) + else: + print('Hybrid module time and overall time have different time units, please check.') + time_ratio = 0. + + # Experiment info + n_epochs = quantum_module["module_config"]["n_epochs"] + setup_info = results[0]["module"]["submodule"]["module_config"] + n_classes = setup_info["n_classes"] + dataset = setup_info["data_set"] + + metrics_vector = [ + time_ratio, + quantum_module["train_accuracy"], + quantum_module["val_accuracy"], + quantum_module["expressibility_jsd"], + quantum_module["meyer-wallach"]] + metrics_names = ['Time ratio', 'Acc_train', 'Acc_test', 'Expressibility', 'Entanglement'] + plt_title = f"QNN: {dataset} (Cls={n_classes})" + info_str = f"Epochs: {n_epochs}, Noise: { + setup_info['noise_sigma']}, Images: { + setup_info['n_images_per_class']}" + + else: + print(f"{m_name} is not implemented for plotting, no radar plot generated.") + return + + # Make metrics plot + Plotter.make_radar_chart(plt_title, info_str, store_dir, metrics_vector, metrics_names) + + logging.info(f"Saved {f'{store_dir}/metrics_collection_test.pdf'}.") diff --git a/src/quark2_adapter/adapters.py b/src/quark2_adapter/adapters.py index 6501e2d44..f6f2b3a72 100644 --- a/src/quark2_adapter/adapters.py +++ b/src/quark2_adapter/adapters.py @@ -13,20 +13,21 @@ # limitations under the License. # +from abc import ABC import json import logging -from abc import ABC from time import time -from modules.applications.Application import Application as Application_NEW -from modules.applications.Mapping import Mapping as Mapping_NEW -from modules.Core import Core -from modules.devices.Device import Device as Device_NEW -from modules.solvers.Solver import Solver as Solver_NEW -from quark2_adapter.legacy_classes.Application import Application as Application_OLD -from quark2_adapter.legacy_classes.Mapping import Mapping as Mapping_OLD -from quark2_adapter.legacy_classes.Solver import Solver as Solver_OLD -from quark2_adapter.legacy_classes.Device import Device as Device_OLD +from modules.core import Core +from modules.applications.application import Application as Application_NEW +from modules.applications.mapping import Mapping as Mapping_NEW +from modules.solvers.solver import Solver as Solver_NEW +from modules.devices.device import Device as Device_NEW +from quark2_adapter.legacy_classes.application import Application as Application_OLD +from quark2_adapter.legacy_classes.mapping import Mapping as Mapping_OLD +from quark2_adapter.legacy_classes.solver import Solver as Solver_OLD +from quark2_adapter.legacy_classes.device import Device as Device_OLD + WARNING_MSG = 'Class "%s" is inheriting from deprecated base class. Please refactor your class.' diff --git a/src/quark2_adapter/legacy_classes/Application.py b/src/quark2_adapter/legacy_classes/application.py similarity index 100% rename from src/quark2_adapter/legacy_classes/Application.py rename to src/quark2_adapter/legacy_classes/application.py diff --git a/src/quark2_adapter/legacy_classes/Device.py b/src/quark2_adapter/legacy_classes/device.py similarity index 100% rename from src/quark2_adapter/legacy_classes/Device.py rename to src/quark2_adapter/legacy_classes/device.py diff --git a/src/quark2_adapter/legacy_classes/Mapping.py b/src/quark2_adapter/legacy_classes/mapping.py similarity index 100% rename from src/quark2_adapter/legacy_classes/Mapping.py rename to src/quark2_adapter/legacy_classes/mapping.py diff --git a/src/quark2_adapter/legacy_classes/Solver.py b/src/quark2_adapter/legacy_classes/solver.py similarity index 100% rename from src/quark2_adapter/legacy_classes/Solver.py rename to src/quark2_adapter/legacy_classes/solver.py diff --git a/src/utils.py b/src/utils.py index b0615b736..7fec39388 100644 --- a/src/utils.py +++ b/src/utils.py @@ -110,8 +110,7 @@ def checkbox(key: str, message: str, choices: list) -> dict: answer = inquirer.prompt([inquirer.Checkbox(key, message=message, choices=choices)]) else: if len(choices) == 1: - logging.info(f"Skipping asking for submodule" - f"since only 1 option ({choices[0]}) is available.") + logging.info(f"Skipping asking for submodule since only 1 option ({choices[0]}) is available.") return {key: choices} if not answer[key]: diff --git a/tests/configs/invalid/TSP.yml b/tests/configs/invalid/tsp.yml similarity index 100% rename from tests/configs/invalid/TSP.yml rename to tests/configs/invalid/tsp.yml diff --git a/tests/configs/valid/ACL.yml b/tests/configs/valid/acl.yml similarity index 100% rename from tests/configs/valid/ACL.yml rename to tests/configs/valid/acl.yml diff --git a/tests/configs/valid/bp.yml b/tests/configs/valid/bp.yml new file mode 100644 index 000000000..9a0ec19eb --- /dev/null +++ b/tests/configs/valid/bp.yml @@ -0,0 +1,37 @@ +application: + config: + instance_creating_mode: + - linear weights without incompatibilities + number_of_objects: + - 3 + name: BP + submodules: + - config: {} + name: MIP + submodules: + - config: + mip_gap: + - 0 + solution_method: + - -1 + time_limit: + - 7200 + name: MIPSolver + submodules: + - config: {} + name: Local + submodules: [] + - config: + penalty_factor: + - 1.0 + name: QUBO + submodules: + - config: + number_of_reads: + - 100 + name: Annealer + submodules: + - config: {} + name: Simulated Annealer + submodules: [] +repetitions: 1 diff --git a/tests/configs/valid/GenerativeModeling.yml b/tests/configs/valid/generative_modeling.yml similarity index 99% rename from tests/configs/valid/GenerativeModeling.yml rename to tests/configs/valid/generative_modeling.yml index 73d927c67..68de3dd8e 100644 --- a/tests/configs/valid/GenerativeModeling.yml +++ b/tests/configs/valid/generative_modeling.yml @@ -6,7 +6,7 @@ application: submodules: - config: data_set: - - MG_2D + - mg_2d train_size: - 0.1 - 1.0 diff --git a/tests/configs/valid/MIS.yml b/tests/configs/valid/mis.yml similarity index 100% rename from tests/configs/valid/MIS.yml rename to tests/configs/valid/mis.yml diff --git a/tests/configs/valid/PVC.yml b/tests/configs/valid/pvc.yml similarity index 100% rename from tests/configs/valid/PVC.yml rename to tests/configs/valid/pvc.yml diff --git a/tests/configs/valid/salbp.yml b/tests/configs/valid/salbp.yml new file mode 100644 index 000000000..2bb003010 --- /dev/null +++ b/tests/configs/valid/salbp.yml @@ -0,0 +1,23 @@ +application: + config: + instance: + - example_instance_n=3.alb + - example_instance_n=10.alb + name: SALBP + submodules: + - config: {} + name: MIP + submodules: + - config: + mip_gap: + - 0 + solution_method: + - -1 + time_limit: + - 7200 + name: MIPSolver + submodules: + - config: {} + name: Local + submodules: [] +repetitions: 2 diff --git a/tests/configs/valid/SAT.yml b/tests/configs/valid/sat.yml similarity index 100% rename from tests/configs/valid/SAT.yml rename to tests/configs/valid/sat.yml diff --git a/tests/configs/valid/SCP.yml b/tests/configs/valid/scp.yml similarity index 100% rename from tests/configs/valid/SCP.yml rename to tests/configs/valid/scp.yml diff --git a/tests/configs/valid/TSP.yml b/tests/configs/valid/tsp.yml similarity index 100% rename from tests/configs/valid/TSP.yml rename to tests/configs/valid/tsp.yml diff --git a/tests/modules/applications/optimization/ACL/mappings/__init__.py b/tests/modules/applications/optimization/acl/__init__.py similarity index 100% rename from tests/modules/applications/optimization/ACL/mappings/__init__.py rename to tests/modules/applications/optimization/acl/__init__.py diff --git a/tests/modules/applications/optimization/MIS/__init__.py b/tests/modules/applications/optimization/acl/mappings/__init__.py similarity index 100% rename from tests/modules/applications/optimization/MIS/__init__.py rename to tests/modules/applications/optimization/acl/mappings/__init__.py diff --git a/tests/modules/applications/optimization/ACL/mappings/test_ISING.py b/tests/modules/applications/optimization/acl/mappings/test_ising.py similarity index 98% rename from tests/modules/applications/optimization/ACL/mappings/test_ISING.py rename to tests/modules/applications/optimization/acl/mappings/test_ising.py index 210182207..b1582f8bc 100644 --- a/tests/modules/applications/optimization/ACL/mappings/test_ISING.py +++ b/tests/modules/applications/optimization/acl/mappings/test_ising.py @@ -3,7 +3,7 @@ import numpy as np from qiskit_optimization import QuadraticProgram -from modules.applications.optimization.ACL.mappings.ISING import Ising +from modules.applications.optimization.acl.mappings.ising import Ising class TestIsing(unittest.TestCase): diff --git a/tests/modules/applications/optimization/ACL/mappings/test_QUBO.py b/tests/modules/applications/optimization/acl/mappings/test_qubo.py similarity index 98% rename from tests/modules/applications/optimization/ACL/mappings/test_QUBO.py rename to tests/modules/applications/optimization/acl/mappings/test_qubo.py index efeb0fe3a..a168e678c 100644 --- a/tests/modules/applications/optimization/ACL/mappings/test_QUBO.py +++ b/tests/modules/applications/optimization/acl/mappings/test_qubo.py @@ -3,7 +3,7 @@ import numpy as np from qiskit_optimization import QuadraticProgram -from modules.applications.optimization.ACL.mappings.QUBO import Qubo +from modules.applications.optimization.acl.mappings.qubo import Qubo class TestQubo(unittest.TestCase): diff --git a/tests/modules/applications/optimization/ACL/test_ACL.py b/tests/modules/applications/optimization/acl/test_acl.py similarity index 97% rename from tests/modules/applications/optimization/ACL/test_ACL.py rename to tests/modules/applications/optimization/acl/test_acl.py index 66b71fbbd..7bb1daa0a 100644 --- a/tests/modules/applications/optimization/ACL/test_ACL.py +++ b/tests/modules/applications/optimization/acl/test_acl.py @@ -1,10 +1,8 @@ -import os import unittest -from tempfile import TemporaryDirectory - +import os import pandas as pd - -from modules.applications.optimization.ACL.ACL import ACL +from tempfile import TemporaryDirectory +from modules.applications.optimization.acl.acl import ACL class TestACL(unittest.TestCase): @@ -109,7 +107,7 @@ def test_generate_tiny_model(self): def test_validate(self): # Create a mock solution mock_solution = {"status": "Optimal"} - is_valid, _ = self.acl_instance.validate(mock_solution) + is_valid, validation_time = self.acl_instance.validate(mock_solution) self.assertTrue(is_valid, "Expected solution to be valid.") invalid_solution = {"status": "Infeasible"} diff --git a/tests/modules/applications/optimization/MIS/mappings/__init__.py b/tests/modules/applications/optimization/bp/__init__.py similarity index 100% rename from tests/modules/applications/optimization/MIS/mappings/__init__.py rename to tests/modules/applications/optimization/bp/__init__.py diff --git a/tests/modules/applications/optimization/PVC/__init__.py b/tests/modules/applications/optimization/bp/mappings/__init__.py similarity index 100% rename from tests/modules/applications/optimization/PVC/__init__.py rename to tests/modules/applications/optimization/bp/mappings/__init__.py diff --git a/tests/modules/applications/optimization/bp/mappings/test_ising.py b/tests/modules/applications/optimization/bp/mappings/test_ising.py new file mode 100644 index 000000000..8876828ee --- /dev/null +++ b/tests/modules/applications/optimization/bp/mappings/test_ising.py @@ -0,0 +1,77 @@ +import unittest +from unittest.mock import patch, MagicMock +import numpy as np + +from modules.applications.optimization.bp.mappings.ising import Ising + + +class TestIsing(unittest.TestCase): + + @classmethod + def setUpClass(cls): + cls.ising_instance = Ising() + + def test_initialization(self): + self.assertListEqual(self.ising_instance.submodule_options, ["QAOA", "PennylaneQAOA", "QiskitQAOA"]) + self.assertIsNone(self.ising_instance.key_mapping) + self.assertIsNone(self.ising_instance.graph) + self.assertIsNone(self.ising_instance.config) + + def test_get_requirements(self): + requirements = self.ising_instance.get_requirements() + expected_requirements = [{"name": "numpy", "version": "1.26.4"}, {"name": "docplex", "version": "2.25.236"}] + self.assertEqual(requirements, expected_requirements) + + def test_get_parameter_options(self): + params = self.ising_instance.get_parameter_options() + self.assertIn("penalty_factor", params) + self.assertEqual(params["penalty_factor"]["values"], [2]) + self.assertIsInstance(params["penalty_factor"]["values"], list) + + def test_map(self): + """ + Test mapping function for bin-packing to Ising formulation. + """ + config = {"penalty_factor": 2} + problem = ([2, 4, 6], 10, []) + with patch("modules.applications.optimization.bp.mappings.mip.MIP.create_mip", return_value=MagicMock()): + with patch("modules.applications.optimization.bp.mappings.ising.Ising.transform_docplex_mip_to_ising", return_value=(np.array([[1, -1], [-1, 1]]), np.array([1, -1]), 0, MagicMock())): + result, _ = self.ising_instance.map(problem, config) + + self.assertIn("J", result) + self.assertIn("t", result) + self.assertIn("c", result) + self.assertIn("QUBO", result) + self.assertIsInstance(result["J"], np.ndarray) + self.assertIsInstance(result["t"], np.ndarray) + self.assertIsInstance(result["c"], (float, int)) + + def test_reverse_map(self): + solution = np.array([1, 0, -1]) + mock_qubo = MagicMock() + mock_qubo.variables = [MagicMock(name=f"x{i}") for i in range(3)] + self.ising_instance.qubo = mock_qubo # Mock the QUBO instance + + result, _ = self.ising_instance.reverse_map(solution) + self.assertIsInstance(result, dict) + self.assertEqual(len(result), 3) + + def test_convert_ising_to_qubo(self): + solution = np.array([1, 0, -1]) + qubo_solution = self.ising_instance._convert_ising_to_qubo(solution) + self.assertIsInstance(qubo_solution, np.ndarray) + self.assertEqual(len(qubo_solution), len(solution)) + self.assertTrue(all(x in [0, 1] for x in qubo_solution)) + + def test_get_default_submodule(self): + submodule = self.ising_instance.get_default_submodule("QAOA") + self.assertIsNotNone(submodule, "Expected 'QAOA' submodule to be returned.") + + submodule = self.ising_instance.get_default_submodule("PennylaneQAOA") + self.assertIsNotNone(submodule, "Expected 'PennylaneQAOA' submodule to be returned.") + + submodule = self.ising_instance.get_default_submodule("QiskitQAOA") + self.assertIsNotNone(submodule, "Expected 'QiskitQAOA' submodule to be returned.") + + with self.assertRaises(NotImplementedError): + self.ising_instance.get_default_submodule("InvalidSubmodule") diff --git a/tests/modules/applications/optimization/bp/mappings/test_mip.py b/tests/modules/applications/optimization/bp/mappings/test_mip.py new file mode 100644 index 000000000..f6decb1da --- /dev/null +++ b/tests/modules/applications/optimization/bp/mappings/test_mip.py @@ -0,0 +1,45 @@ +import unittest +from unittest.mock import patch, MagicMock +from docplex.mp.model import Model +from modules.applications.optimization.bp.mappings.mip import MIP + + +class TestMIP(unittest.TestCase): + + @classmethod + def setUpClass(cls): + cls.mip_instance = MIP() + + def test_initialization(self): + self.assertListEqual(self.mip_instance.submodule_options, ["MIPSolver"]) + self.assertIsNone(self.mip_instance.key_mapping) + self.assertIsNone(self.mip_instance.graph) + self.assertIsNone(self.mip_instance.config) + + def test_get_requirements(self): + requirements = self.mip_instance.get_requirements() + self.assertIsInstance(requirements, list, "Expected requirements to be a list.") + self.assertEqual(requirements[0]["name"], "docplex", "Expected first requirement to be 'docplex'.") + self.assertEqual(requirements[0]["version"], "2.25.236", "Expected docplex version to be '2.25.236'.") + + def test_get_parameter_options(self): + params = self.mip_instance.get_parameter_options() + self.assertIsInstance(params, dict, "Expected parameter options to be a dictionary.") + self.assertEqual(len(params), 0, "Expected parameter options to be an empty dictionary.") + + def test_map(self): + config = {"modelling_goal": 1.0} + problem = ([2, 4, 6], 10, []) + + with patch("modules.applications.optimization.bp.mappings.mip.MIP.create_mip", return_value=Model()): + model, processing_time = self.mip_instance.map(problem, config) + + self.assertIsInstance(model, Model, "Expected output to be an instance of Model.") + self.assertIsInstance(processing_time, float, "Expected processing_time to be a float.") + + def test_get_default_submodule(self): + submodule = self.mip_instance.get_default_submodule("MIPSolver") + self.assertIsNotNone(submodule, "Expected 'MIPSolver' submodule to be returned.") + + with self.assertRaises(NotImplementedError): + self.mip_instance.get_default_submodule("InvalidSubmodule") diff --git a/tests/modules/applications/optimization/bp/mappings/test_qubo.py b/tests/modules/applications/optimization/bp/mappings/test_qubo.py new file mode 100644 index 000000000..44a8e9810 --- /dev/null +++ b/tests/modules/applications/optimization/bp/mappings/test_qubo.py @@ -0,0 +1,46 @@ +import unittest +from unittest.mock import patch, MagicMock +from modules.applications.optimization.bp.mappings.qubo import QUBO + + +class TestQUBO(unittest.TestCase): + + @classmethod + def setUpClass(cls): + cls.qubo_instance = QUBO() + + def test_initialization(self): + self.assertListEqual(self.qubo_instance.submodule_options, ["Annealer"]) + + def test_get_requirements(self): + requirements = self.qubo_instance.get_requirements() + expected_requirements = [{"name": "numpy", "version": "1.26.4"}, {"name": "docplex", "version": "2.25.236"}] + self.assertEqual(requirements, expected_requirements) + + def test_get_parameter_options(self): + params = self.qubo_instance.get_parameter_options() + self.assertIn("penalty_factor", params, "Expected 'penalty_factor' in parameter options.") + self.assertEqual(params["penalty_factor"]["values"], [1], "Expected penalty_factor values to be [1].") + self.assertTrue(params["penalty_factor"]["custom_input"], "Expected custom_input to be True.") + self.assertTrue(params["penalty_factor"]["allow_ranges"], "Expected allow_ranges to be True.") + + def test_map(self): + config = {"penalty_factor": 2} + problem = ([2, 4, 6], 10, []) + + with patch("modules.applications.optimization.bp.mappings.mip.MIP.create_mip", return_value=MagicMock()): + with patch("modules.applications.optimization.bp.mappings.qubo.QUBO.transform_docplex_mip_to_qubo", + return_value=(MagicMock(), MagicMock())): + result, _ = self.qubo_instance.map(problem, config) + + self.assertIn("Q", result, "Expected 'Q' (QUBO operator) in result.") + self.assertIn("QUBO", result, "Expected 'QUBO' representation in result.") + self.assertIsNotNone(result["Q"], "Expected 'Q' to be non-empty.") + self.assertIsNotNone(result["QUBO"], "Expected 'QUBO' to be non-empty.") + + def test_get_default_submodule(self): + submodule = self.qubo_instance.get_default_submodule("Annealer") + self.assertIsNotNone(submodule, "Expected 'Annealer' submodule to be returned.") + + with self.assertRaises(NotImplementedError): + self.qubo_instance.get_default_submodule("InvalidSubmodule") diff --git a/tests/modules/applications/optimization/bp/test_bp.py b/tests/modules/applications/optimization/bp/test_bp.py new file mode 100644 index 000000000..b080be8f3 --- /dev/null +++ b/tests/modules/applications/optimization/bp/test_bp.py @@ -0,0 +1,110 @@ +import unittest +from unittest.mock import MagicMock, patch +import numpy as np +from qiskit_optimization import QuadraticProgram + +from docplex.mp.model import Model +from modules.applications.optimization.bp.bp import BP +from modules.applications.optimization.bp.mappings.mip import MIP +from modules.applications.optimization.bp.mappings.ising import Ising +from modules.applications.optimization.bp.mappings.qubo import QUBO + + +class TestBP(unittest.TestCase): + + @classmethod + def setUpClass(cls): + cls.bp_instance = BP() + + def setUp(self): + self.bp_instance.generate_problem({ + "number_of_objects": 5, + "instance_creating_mode": "linear weights without incompatibilities" + }) + + @classmethod + def tearDownClass(cls): + del cls.bp_instance + + def test_initialization(self): + self.assertEqual(self.bp_instance.name, "BinPacking") + self.assertEqual(self.bp_instance.submodule_options, ["MIP", "Ising", "QUBO"]) + + def test_get_requirements(self): + requirements = self.bp_instance.get_requirements() + expected_requirements = [ + {"name": "numpy", "version": "1.26.4"}, + {"name": "qiskit_optimization", "version": "0.6.1"}, + {"name": "docplex", "version": "2.25.236"} + ] + self.assertEqual(requirements, expected_requirements) + + def test_get_solution_quality_unit(self): + self.assertEqual(self.bp_instance.get_solution_quality_unit(), "number_of_bins") + + def test_get_parameter_options(self): + params = self.bp_instance.get_parameter_options() + self.assertIn("number_of_objects", params) + self.assertIn("instance_creating_mode", params) + self.assertIsInstance(params["number_of_objects"]["values"], list) + self.assertIsInstance(params["instance_creating_mode"]["values"], list) + + def test_create_bin_packing_instance(self): + object_weights, bin_capacity, incompatible_objects = self.bp_instance.create_bin_packing_instance( + number_of_objects=5, mode="linear weights without incompatibilities" + ) + + self.assertEqual(bin_capacity, 5) + self.assertEqual(len(object_weights), 5) + self.assertEqual(incompatible_objects, []) + + def test_generate_problem(self): + config = { + "number_of_objects": 5, + "instance_creating_mode": "linear weights without incompatibilities" + } + object_weights, bin_capacity, incompatible_objects = self.bp_instance.generate_problem(config) + + self.assertEqual(len(object_weights), 5) + self.assertEqual(bin_capacity, 5) + self.assertEqual(incompatible_objects, []) + + def test_validate_invalid_solution(self): + solution = None # Invalid solution case + + validity, _ = self.bp_instance.validate(solution) + self.assertFalse(validity) + + def test_validate_valid_solution(self): + solution = {f"x_{i}": 1 for i in range(30)} + + validity, _ = self.bp_instance.validate(solution) + self.assertIsInstance(validity, bool) + + def test_evaluate_solution(self): + solution = {f"x_{i}": 1 for i in range(5)} + + self.bp_instance.mip_qiskit = MagicMock() + self.bp_instance.mip_qiskit.objective.evaluate.return_value = 10.0 + + obj_value, _ = self.bp_instance.evaluate(solution) + self.assertIsInstance(obj_value, (float, int)) + + def test_create_mip(self): + problem = ([2, 4, 6], 10, []) + model = MIP.create_mip(self, problem) + + self.assertIsInstance(model, Model) + self.assertTrue(model.get_objective_expr() is not None) + + def test_transform_docplex_mip_to_qubo(self): + model = Model() + model.binary_var(name="x1") + model.binary_var(name="x2") + + with patch("modules.applications.optimization.bp.bp.from_docplex_mp", return_value=QuadraticProgram()): + qubo_instance = QUBO() + qubo_operator, qubo = qubo_instance.transform_docplex_mip_to_qubo(model, penalty_factor=1.0) + + self.assertIsInstance(qubo_operator, dict) + self.assertIsInstance(qubo, QuadraticProgram) diff --git a/tests/modules/applications/optimization/PVC/mappings/__init__.py b/tests/modules/applications/optimization/mis/__init__.py similarity index 100% rename from tests/modules/applications/optimization/PVC/mappings/__init__.py rename to tests/modules/applications/optimization/mis/__init__.py diff --git a/tests/modules/applications/optimization/SAT/__init__.py b/tests/modules/applications/optimization/mis/mappings/__init__.py similarity index 100% rename from tests/modules/applications/optimization/SAT/__init__.py rename to tests/modules/applications/optimization/mis/mappings/__init__.py diff --git a/tests/modules/applications/optimization/MIS/mappings/MIS_test_graph.pkl b/tests/modules/applications/optimization/mis/mappings/mis_test_graph.pkl similarity index 100% rename from tests/modules/applications/optimization/MIS/mappings/MIS_test_graph.pkl rename to tests/modules/applications/optimization/mis/mappings/mis_test_graph.pkl diff --git a/tests/modules/applications/optimization/MIS/mappings/test_NeutralAtom.py b/tests/modules/applications/optimization/mis/mappings/test_neutral_atom.py similarity index 89% rename from tests/modules/applications/optimization/MIS/mappings/test_NeutralAtom.py rename to tests/modules/applications/optimization/mis/mappings/test_neutral_atom.py index 698606ab1..bf5a0633f 100644 --- a/tests/modules/applications/optimization/MIS/mappings/test_NeutralAtom.py +++ b/tests/modules/applications/optimization/mis/mappings/test_neutral_atom.py @@ -1,7 +1,7 @@ import pickle import unittest -from modules.applications.optimization.MIS.mappings.NeutralAtom import NeutralAtom +from modules.applications.optimization.mis.mappings.neutral_atom import NeutralAtom class TestNeutralAtom(unittest.TestCase): @@ -9,7 +9,7 @@ class TestNeutralAtom(unittest.TestCase): @classmethod def setUpClass(cls): cls.neutral_atom_instance = NeutralAtom() - with open("tests/modules/applications/optimization/MIS/mappings/MIS_test_graph.pkl", "rb") as file: + with open("tests/modules/applications/optimization/mis/mappings/mis_test_graph.pkl", "rb") as file: cls.graph = pickle.load(file) cls.config = {} diff --git a/tests/modules/applications/optimization/MIS/test_MIS.py b/tests/modules/applications/optimization/mis/test_mis.py similarity index 98% rename from tests/modules/applications/optimization/MIS/test_MIS.py rename to tests/modules/applications/optimization/mis/test_mis.py index cf3159f48..6b7a60289 100644 --- a/tests/modules/applications/optimization/MIS/test_MIS.py +++ b/tests/modules/applications/optimization/mis/test_mis.py @@ -4,7 +4,7 @@ import networkx as nx -from modules.applications.optimization.MIS.MIS import MIS +from modules.applications.optimization.mis.mis import MIS class TestMIS(unittest.TestCase): diff --git a/tests/modules/applications/optimization/SAT/mappings/__init__.py b/tests/modules/applications/optimization/pvc/__init__.py similarity index 100% rename from tests/modules/applications/optimization/SAT/mappings/__init__.py rename to tests/modules/applications/optimization/pvc/__init__.py diff --git a/tests/modules/applications/optimization/SCP/__init__.py b/tests/modules/applications/optimization/pvc/mappings/__init__.py similarity index 100% rename from tests/modules/applications/optimization/SCP/__init__.py rename to tests/modules/applications/optimization/pvc/mappings/__init__.py diff --git a/tests/modules/applications/optimization/PVC/mappings/pvc_graph_1_seam.gpickle b/tests/modules/applications/optimization/pvc/mappings/pvc_graph_1_seam.gpickle similarity index 100% rename from tests/modules/applications/optimization/PVC/mappings/pvc_graph_1_seam.gpickle rename to tests/modules/applications/optimization/pvc/mappings/pvc_graph_1_seam.gpickle diff --git a/tests/modules/applications/optimization/PVC/mappings/test_ISING.py b/tests/modules/applications/optimization/pvc/mappings/test_ising.py similarity index 93% rename from tests/modules/applications/optimization/PVC/mappings/test_ISING.py rename to tests/modules/applications/optimization/pvc/mappings/test_ising.py index 6d8a16753..323974eb1 100644 --- a/tests/modules/applications/optimization/PVC/mappings/test_ISING.py +++ b/tests/modules/applications/optimization/pvc/mappings/test_ising.py @@ -3,8 +3,8 @@ import numpy as np -from modules.applications.optimization.PVC.mappings.ISING import Ising -from modules.applications.optimization.PVC.mappings.QUBO import QUBO +from modules.applications.optimization.pvc.mappings.ising import Ising +from modules.applications.optimization.pvc.mappings.qubo import QUBO class TestIsing(unittest.TestCase): @@ -12,7 +12,7 @@ class TestIsing(unittest.TestCase): @classmethod def setUpClass(cls): cls.ising_instance = Ising() - with open("tests/modules/applications/optimization/PVC/mappings/pvc_graph_1_seam.gpickle", "rb") as file: + with open("tests/modules/applications/optimization/pvc/mappings/pvc_graph_1_seam.gpickle", "rb") as file: cls.graph = pickle.load(file) def test_get_requirements(self): diff --git a/tests/modules/applications/optimization/PVC/mappings/test_QUBO.py b/tests/modules/applications/optimization/pvc/mappings/test_qubo.py similarity index 93% rename from tests/modules/applications/optimization/PVC/mappings/test_QUBO.py rename to tests/modules/applications/optimization/pvc/mappings/test_qubo.py index 198a39463..3837f64ea 100644 --- a/tests/modules/applications/optimization/PVC/mappings/test_QUBO.py +++ b/tests/modules/applications/optimization/pvc/mappings/test_qubo.py @@ -1,7 +1,7 @@ import pickle import unittest -from modules.applications.optimization.PVC.mappings.QUBO import QUBO +from modules.applications.optimization.pvc.mappings.qubo import QUBO class TestQUBO(unittest.TestCase): @@ -9,7 +9,7 @@ class TestQUBO(unittest.TestCase): @classmethod def setUpClass(cls): cls.qubo_instance = QUBO() - with open("tests/modules/applications/optimization/PVC/mappings/pvc_graph_1_seam.gpickle", "rb") as file: + with open("tests/modules/applications/optimization/pvc/mappings/pvc_graph_1_seam.gpickle", "rb") as file: cls.graph = pickle.load(file) def test_get_requirements(self): diff --git a/tests/modules/applications/optimization/PVC/test_PVC.py b/tests/modules/applications/optimization/pvc/test_pvc.py similarity index 98% rename from tests/modules/applications/optimization/PVC/test_PVC.py rename to tests/modules/applications/optimization/pvc/test_pvc.py index 9f11b77b3..4f7e15ce0 100644 --- a/tests/modules/applications/optimization/PVC/test_PVC.py +++ b/tests/modules/applications/optimization/pvc/test_pvc.py @@ -4,7 +4,7 @@ from networkx import Graph -from modules.applications.optimization.PVC.PVC import PVC +from modules.applications.optimization.pvc.pvc import PVC class TestPVC(unittest.TestCase): diff --git a/tests/modules/applications/optimization/SCP/mappings/__init__.py b/tests/modules/applications/optimization/salbp/__init__.py similarity index 100% rename from tests/modules/applications/optimization/SCP/mappings/__init__.py rename to tests/modules/applications/optimization/salbp/__init__.py diff --git a/tests/modules/applications/optimization/salbp/mappings/test_mip.py b/tests/modules/applications/optimization/salbp/mappings/test_mip.py new file mode 100644 index 000000000..6756a9f22 --- /dev/null +++ b/tests/modules/applications/optimization/salbp/mappings/test_mip.py @@ -0,0 +1,96 @@ +import unittest +from unittest.mock import patch, MagicMock +from docplex.mp.model import Model +from docplex.mp.dvar import Var +from modules.applications.optimization.salbp.mappings.mip import MIP +from modules.applications.optimization.salbp.salbp import Task, SALBPInstance + + +class TestMIP(unittest.TestCase): + + @classmethod + def setUpClass(cls): + cls.mip_instance = MIP() + cls.tasks = frozenset({Task(1, 3), Task(2, 2), Task(3, 1)}) + cls.precedences = frozenset({(Task(1, 3), Task(2, 2))}) + cls.salbp_instance = SALBPInstance(cycle_time=5, tasks=cls.tasks, preceding_tasks=cls.precedences) + + def test_initialization(self): + self.assertListEqual(self.mip_instance.submodule_options, ["MIPSolver"]) + self.assertIsNone(self.mip_instance.salbp, "Expected salbp to be None.") + self.assertIsNone(self.mip_instance.config, "Expected config to be None.") + + def test_get_requirements(self): + requirements = self.mip_instance.get_requirements() + self.assertIsInstance(requirements, list, "Expected requirements to be a list.") + self.assertEqual(requirements[0]["name"], "docplex", "Expected first requirement to be 'docplex'.") + self.assertEqual(requirements[0]["version"], "2.25.236", "Expected docplex version to be '2.25.236'.") + + def test_get_parameter_options(self): + params = self.mip_instance.get_parameter_options() + self.assertIsInstance(params, dict, "Expected parameter options to be a dictionary.") + self.assertEqual(len(params), 0, "Expected parameter options to be an empty dictionary.") + + def test_map(self): + config = {} + with patch("modules.applications.optimization.salbp.mappings.mip.Model", return_value=Model()): + model, processing_time = self.mip_instance.map(self.salbp_instance, config) + + self.assertIsInstance(model, Model, "Expected output to be an instance of Model.") + self.assertIsInstance(processing_time, float, "Expected processing_time to be a float.") + + def test_add_variables(self): + station_vars, task_station_vars = self.mip_instance._add_variables(self.salbp_instance, Model(), 3) + + self.assertIsInstance(station_vars, list, "Expected station variables to be a list.") + self.assertIsInstance(task_station_vars, dict, "Expected task-station variables to be a dictionary.") + self.assertGreater(len(station_vars), 0, "Expected at least one station variable.") + self.assertGreater(len(task_station_vars), 0, "Expected task-station assignments to exist.") + + def test_add_one_station_per_task_constraints(self): + mock_model = MagicMock() + task_station_vars = {(t.id, s): MagicMock() for t in self.tasks for s in range(3)} + + self.mip_instance._add_one_station_per_task_constraints(mock_model, self.tasks, 3, task_station_vars) + mock_model.add_constraints.assert_called() + + def test_add_cycle_time_constraints(self): + mock_model = MagicMock() + task_station_vars = {(t.id, s): MagicMock() for t in self.tasks for s in range(3)} + station_vars = [MagicMock() for _ in range(3)] + + self.mip_instance._add_cycle_time_constraints( + mock_model, self.salbp_instance, 3, task_station_vars, station_vars) + mock_model.add_constraints.assert_called() + + def test_add_preceding_tasks_constraints(self): + mock_model = MagicMock() + task_station_vars = {(t.id, s): MagicMock() for t in self.tasks for s in range(3)} + + self.mip_instance._add_preceding_tasks_constraints(mock_model, self.salbp_instance, 3, task_station_vars) + mock_model.add_constraints.assert_called() + + def test_add_consecutive_stations_constraints(self): + mock_model = MagicMock() + station_vars = [MagicMock() for _ in range(3)] + + self.mip_instance._add_consecutive_stations_constraints(mock_model, 3, station_vars) + mock_model.add_constraints.assert_called() + + def test_reverse_map(self): + solution = {"y_1": 1, "y_2": 1, "x_1_1": 1, "x_2_2": 1} + self.mip_instance.salbp = self.salbp_instance + + with patch.object(self.salbp_instance, "get_task", side_effect=lambda x: Task(x, 2)): + task_assignment, _ = self.mip_instance.reverse_map(solution) + + self.assertIsInstance(task_assignment, dict, "Expected task assignment to be a dictionary.") + self.assertIn(1, task_assignment, "Expected station 1 in task assignment.") + self.assertIn(2, task_assignment, "Expected station 2 in task assignment.") + + def test_get_default_submodule(self): + submodule = self.mip_instance.get_default_submodule("MIPSolver") + self.assertIsNotNone(submodule, "Expected 'MIPSolver' submodule to be returned.") + + with self.assertRaises(NotImplementedError): + self.mip_instance.get_default_submodule("InvalidSubmodule") diff --git a/tests/modules/applications/optimization/salbp/test_salbp.py b/tests/modules/applications/optimization/salbp/test_salbp.py new file mode 100644 index 000000000..878f0558c --- /dev/null +++ b/tests/modules/applications/optimization/salbp/test_salbp.py @@ -0,0 +1,131 @@ +import unittest +from unittest.mock import patch, MagicMock +import networkx as nx +from pathlib import Path +from modules.applications.optimization.salbp.salbp import ( + Task, SALBPInstance, salbp_factory, has_overloaded_station, has_unique_assignment_for_every_task, + respects_precedences, parse_task, create_salbp_from_file, SALBP +) + + +class TestSALBP(unittest.TestCase): + + @classmethod + def setUpClass(cls): + cls.tasks = {Task(1, 3), Task(2, 2), Task(3, 1)} + cls.precedences = {(Task(1, 3), Task(2, 2))} + cls.salbp_instance = SALBPInstance(cycle_time=5, tasks=cls.tasks, preceding_tasks=cls.precedences) + + def test_task_creation(self): + task = Task(1, 5) + self.assertEqual(task.id, 1, "Expected task ID to be 1") + self.assertEqual(task.time, 5, "Expected task time to be 5") + + def test_salbp_instance_creation(self): + self.assertEqual(self.salbp_instance.cycle_time, 5, "Expected cycle time to be 5") + self.assertEqual(len(self.salbp_instance.tasks), 3, "Expected 3 tasks in instance") + self.assertEqual(len(self.salbp_instance.preceding_tasks), 1, "Expected 1 precedence constraint") + + def test_salbp_factory(self): + instance = salbp_factory(list(self.tasks), list(self.precedences), 5) + self.assertIsInstance(instance, SALBPInstance, "Expected instance to be of type SALBPInstance") + + def test_has_overloaded_station(self): + task_assignment = {1: [Task(1, 3), Task(2, 4)]} + self.assertTrue(has_overloaded_station(5, task_assignment), "Expected station to be overloaded") + + def test_has_unique_assignment_for_every_task(self): + task_assignment = { + 1: [Task(1, 3)], + 2: [Task(2, 2)], + 3: [Task(3, 1)] + } + self.assertTrue(has_unique_assignment_for_every_task(self.tasks, task_assignment), + "Expected all tasks to be uniquely assigned") + + def test_respects_precedences(self): + task_assignment = {1: [Task(1, 3)], 2: [Task(2, 2)]} + self.assertTrue(respects_precedences(self.precedences, task_assignment), + "Expected precedences to be respected") + + def test_parse_task(self): + task = parse_task("1 5") + self.assertIsInstance(task, Task, "Expected a Task instance") + self.assertEqual(task.id, 1, "Expected task ID to be 1") + self.assertEqual(task.time, 5, "Expected task time to be 5") + + def test_create_salbp_from_file(self): + mock_data = [ + "", "3", + "", "5", + "", "1 3", "2 2", "3 1", + "", + "1,2", + "2,3", + "" + ] + + mock_indices = { + "": 0, + "": 2, + "": 4, + "": 7, + "": 10 + } + + mock_split_lines = { + "": ["3"], + "": ["5"], + "": ["1 3", "2 2", "3 1"], + "": ["1,2", "2,3"], + } + + with patch("modules.applications.optimization.salbp.salbp.read_data", return_value=mock_data): + with patch("modules.applications.optimization.salbp.salbp.get_indices", return_value=mock_indices): + with patch("modules.applications.optimization.salbp.salbp.split_lines_to_areas", return_value=mock_split_lines): + instance = create_salbp_from_file(Path("mock_path")) + self.assertIsInstance(instance, SALBPInstance, "Expected a valid SALBPInstance") + + def test_salbp_initialization(self): + salbp = SALBP() + self.assertEqual(salbp.name, "SALBP", "Expected name to be 'SALBP'") + self.assertEqual(salbp.submodule_options, ["MIP"], "Expected submodule options to contain 'MIP'") + + def test_get_requirements(self): + salbp = SALBP() + requirements = salbp.get_requirements() + expected_requirements = [ + {"name": "docplex", "version": "2.25.236"}, + {"name": "networkx", "version": "3.4.2"}, + ] + self.assertEqual(requirements, expected_requirements, "Expected correct module dependencies") + + def test_generate_problem(self): + salbp = SALBP() + with patch("modules.applications.optimization.salbp.salbp.create_salbp_from_file", return_value=self.salbp_instance): + instance = salbp.generate_problem({"instance": "example_instance_n=3.alb"}) + self.assertIsInstance(instance, SALBPInstance, "Expected a valid SALBPInstance") + + def test_validate_solution(self): + salbp = SALBP() + salbp.salbp = self.salbp_instance + solution = {1: [Task(1, 3)], 2: [Task(2, 2)], 3: [Task(3, 1)]} + + with patch("modules.applications.optimization.salbp.salbp.has_overloaded_station", return_value=False): + with patch("modules.applications.optimization.salbp.salbp.has_unique_assignment_for_every_task", + return_value=True): + with patch("modules.applications.optimization.salbp.salbp.respects_precedences", return_value=True): + validity, _ = salbp.validate(solution) + self.assertTrue(validity, "Expected solution to be valid") + + def test_evaluate_solution(self): + """ + Test evaluation function. + """ + salbp = SALBP() + salbp.salbp = self.salbp_instance + solution = {1: [Task(1, 3)], 2: [Task(2, 2)], 3: [Task(3, 1)]} + salbp.task_assignment = solution + + obj_value, _ = salbp.evaluate(solution) + self.assertEqual(obj_value, 3, "Expected objective value to be 3 (number of used stations)") diff --git a/tests/modules/applications/optimization/TSP/__init__.py b/tests/modules/applications/optimization/sat/__init__.py similarity index 100% rename from tests/modules/applications/optimization/TSP/__init__.py rename to tests/modules/applications/optimization/sat/__init__.py diff --git a/tests/modules/applications/optimization/TSP/mappings/__init__.py b/tests/modules/applications/optimization/sat/mappings/__init__.py similarity index 100% rename from tests/modules/applications/optimization/TSP/mappings/__init__.py rename to tests/modules/applications/optimization/sat/mappings/__init__.py diff --git a/tests/modules/applications/optimization/SAT/mappings/test_ChoiISING.py b/tests/modules/applications/optimization/sat/mappings/test_choiIsing.py similarity index 97% rename from tests/modules/applications/optimization/SAT/mappings/test_ChoiISING.py rename to tests/modules/applications/optimization/sat/mappings/test_choiIsing.py index 12e30512f..f914aae8d 100644 --- a/tests/modules/applications/optimization/SAT/mappings/test_ChoiISING.py +++ b/tests/modules/applications/optimization/sat/mappings/test_choiIsing.py @@ -3,7 +3,7 @@ import numpy as np from nnf import And, Or, Var -from modules.applications.optimization.SAT.mappings.ChoiISING import ChoiIsing +from modules.applications.optimization.sat.mappings.choiising import ChoiIsing class TestChoiIsing(unittest.TestCase): diff --git a/tests/modules/applications/optimization/SAT/mappings/test_ChoiQUBO.py b/tests/modules/applications/optimization/sat/mappings/test_choiqubo.py similarity index 97% rename from tests/modules/applications/optimization/SAT/mappings/test_ChoiQUBO.py rename to tests/modules/applications/optimization/sat/mappings/test_choiqubo.py index 4cca6da1e..f5ef28269 100644 --- a/tests/modules/applications/optimization/SAT/mappings/test_ChoiQUBO.py +++ b/tests/modules/applications/optimization/sat/mappings/test_choiqubo.py @@ -2,7 +2,7 @@ from nnf import And, Or, Var -from modules.applications.optimization.SAT.mappings.ChoiQUBO import ChoiQUBO +from modules.applications.optimization.sat.mappings.choiqubo import ChoiQUBO class TestChoiQUBO(unittest.TestCase): diff --git a/tests/modules/applications/optimization/SAT/mappings/test_DinneenISING.py b/tests/modules/applications/optimization/sat/mappings/test_dinneenising.py similarity index 97% rename from tests/modules/applications/optimization/SAT/mappings/test_DinneenISING.py rename to tests/modules/applications/optimization/sat/mappings/test_dinneenising.py index e96e5ba65..a26338e57 100644 --- a/tests/modules/applications/optimization/SAT/mappings/test_DinneenISING.py +++ b/tests/modules/applications/optimization/sat/mappings/test_dinneenising.py @@ -3,7 +3,7 @@ import numpy as np from nnf import And, Or, Var -from modules.applications.optimization.SAT.mappings.DinneenISING import DinneenIsing +from modules.applications.optimization.sat.mappings.dinneenising import DinneenIsing class TestDinneenIsing(unittest.TestCase): diff --git a/tests/modules/applications/optimization/SAT/mappings/test_DinneenQUBO.py b/tests/modules/applications/optimization/sat/mappings/test_dinneenqubo.py similarity index 97% rename from tests/modules/applications/optimization/SAT/mappings/test_DinneenQUBO.py rename to tests/modules/applications/optimization/sat/mappings/test_dinneenqubo.py index d471b70ac..cad2f87c3 100644 --- a/tests/modules/applications/optimization/SAT/mappings/test_DinneenQUBO.py +++ b/tests/modules/applications/optimization/sat/mappings/test_dinneenqubo.py @@ -2,7 +2,7 @@ from nnf import And, Or, Var -from modules.applications.optimization.SAT.mappings.DinneenQUBO import DinneenQUBO +from modules.applications.optimization.sat.mappings.dinneenqubo import DinneenQUBO class TestDinneenQUBO(unittest.TestCase): diff --git a/tests/modules/applications/optimization/SAT/mappings/test_Direct.py b/tests/modules/applications/optimization/sat/mappings/test_direct.py similarity index 98% rename from tests/modules/applications/optimization/SAT/mappings/test_Direct.py rename to tests/modules/applications/optimization/sat/mappings/test_direct.py index 534e13589..75ea8ffc5 100644 --- a/tests/modules/applications/optimization/SAT/mappings/test_Direct.py +++ b/tests/modules/applications/optimization/sat/mappings/test_direct.py @@ -3,7 +3,7 @@ from nnf import And, Or, Var from pysat.formula import WCNF -from modules.applications.optimization.SAT.mappings.Direct import Direct +from modules.applications.optimization.sat.mappings.direct import Direct class TestDirect(unittest.TestCase): diff --git a/tests/modules/applications/optimization/SAT/mappings/test_QubovertQUBO.py b/tests/modules/applications/optimization/sat/mappings/test_qubovertqubo.py similarity index 96% rename from tests/modules/applications/optimization/SAT/mappings/test_QubovertQUBO.py rename to tests/modules/applications/optimization/sat/mappings/test_qubovertqubo.py index f1aefc5e5..0e7886c54 100644 --- a/tests/modules/applications/optimization/SAT/mappings/test_QubovertQUBO.py +++ b/tests/modules/applications/optimization/sat/mappings/test_qubovertqubo.py @@ -2,7 +2,7 @@ from nnf import And, Or, Var -from modules.applications.optimization.SAT.mappings.QubovertQUBO import QubovertQUBO +from modules.applications.optimization.sat.mappings.qubovertqubo import QubovertQUBO class TestQubovertQUBO(unittest.TestCase): diff --git a/tests/modules/applications/optimization/SAT/test_SAT.py b/tests/modules/applications/optimization/sat/test_sat.py similarity index 98% rename from tests/modules/applications/optimization/SAT/test_SAT.py rename to tests/modules/applications/optimization/sat/test_sat.py index e6eb3e185..0195da633 100644 --- a/tests/modules/applications/optimization/SAT/test_SAT.py +++ b/tests/modules/applications/optimization/sat/test_sat.py @@ -1,10 +1,9 @@ -import os import unittest -from tempfile import TemporaryDirectory - +import os import nnf +from tempfile import TemporaryDirectory -from modules.applications.optimization.SAT.SAT import SAT +from modules.applications.optimization.sat.sat import SAT class TestSAT(unittest.TestCase): diff --git a/tests/modules/applications/optimization/scp/__init__.py b/tests/modules/applications/optimization/scp/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/tests/modules/applications/optimization/scp/mappings/__init__.py b/tests/modules/applications/optimization/scp/mappings/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/tests/modules/applications/optimization/SCP/mappings/test_qubovertQUBO.py b/tests/modules/applications/optimization/scp/mappings/test_qubovertqubo.py similarity index 97% rename from tests/modules/applications/optimization/SCP/mappings/test_qubovertQUBO.py rename to tests/modules/applications/optimization/scp/mappings/test_qubovertqubo.py index 65634e0da..8bcd86180 100644 --- a/tests/modules/applications/optimization/SCP/mappings/test_qubovertQUBO.py +++ b/tests/modules/applications/optimization/scp/mappings/test_qubovertqubo.py @@ -1,6 +1,6 @@ import unittest -from modules.applications.optimization.SCP.mappings.qubovertQUBO import QubovertQUBO +from modules.applications.optimization.scp.mappings.qubovertqubo import QubovertQUBO class TestQubovertQUBO(unittest.TestCase): diff --git a/tests/modules/applications/optimization/SCP/test_SCP.py b/tests/modules/applications/optimization/scp/test_scp.py similarity index 98% rename from tests/modules/applications/optimization/SCP/test_SCP.py rename to tests/modules/applications/optimization/scp/test_scp.py index bd880d86e..03e9aacc9 100644 --- a/tests/modules/applications/optimization/SCP/test_SCP.py +++ b/tests/modules/applications/optimization/scp/test_scp.py @@ -3,7 +3,7 @@ import unittest from tempfile import TemporaryDirectory -from modules.applications.optimization.SCP.SCP import SCP +from modules.applications.optimization.scp.scp import SCP class TestSCP(unittest.TestCase): diff --git a/tests/modules/applications/optimization/tsp/__init__.py b/tests/modules/applications/optimization/tsp/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/tests/modules/applications/optimization/tsp/mappings/__init__.py b/tests/modules/applications/optimization/tsp/mappings/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/tests/modules/applications/optimization/TSP/mappings/test_ISING.py b/tests/modules/applications/optimization/tsp/mappings/test_ising.py similarity index 98% rename from tests/modules/applications/optimization/TSP/mappings/test_ISING.py rename to tests/modules/applications/optimization/tsp/mappings/test_ising.py index 56cc61a53..8f2096eed 100644 --- a/tests/modules/applications/optimization/TSP/mappings/test_ISING.py +++ b/tests/modules/applications/optimization/tsp/mappings/test_ising.py @@ -3,7 +3,7 @@ import networkx as nx import numpy as np -from modules.applications.optimization.TSP.mappings.ISING import Ising +from modules.applications.optimization.tsp.mappings.ising import Ising class TestIsing(unittest.TestCase): diff --git a/tests/modules/applications/optimization/TSP/mappings/test_QUBO.py b/tests/modules/applications/optimization/tsp/mappings/test_qubo.py similarity index 96% rename from tests/modules/applications/optimization/TSP/mappings/test_QUBO.py rename to tests/modules/applications/optimization/tsp/mappings/test_qubo.py index 7ac8c3fe2..660529bb4 100644 --- a/tests/modules/applications/optimization/TSP/mappings/test_QUBO.py +++ b/tests/modules/applications/optimization/tsp/mappings/test_qubo.py @@ -2,7 +2,7 @@ import networkx as nx -from modules.applications.optimization.TSP.mappings.QUBO import QUBO +from modules.applications.optimization.tsp.mappings.qubo import QUBO class TestQUBO(unittest.TestCase): diff --git a/tests/modules/applications/optimization/TSP/test_TSP.py b/tests/modules/applications/optimization/tsp/test_tsp.py similarity index 98% rename from tests/modules/applications/optimization/TSP/test_TSP.py rename to tests/modules/applications/optimization/tsp/test_tsp.py index 0a445cb33..78f557c5c 100644 --- a/tests/modules/applications/optimization/TSP/test_TSP.py +++ b/tests/modules/applications/optimization/tsp/test_tsp.py @@ -6,7 +6,7 @@ import networkx as nx import numpy as np -from modules.applications.optimization.TSP.TSP import TSP +from modules.applications.optimization.tsp.tsp import TSP class TestTSP(unittest.TestCase): diff --git a/tests/modules/applications/qml/generative_modeling/circuits/test_CircuitCardinality.py b/tests/modules/applications/qml/generative_modeling/circuits/test_circuit_cardinality.py similarity index 97% rename from tests/modules/applications/qml/generative_modeling/circuits/test_CircuitCardinality.py rename to tests/modules/applications/qml/generative_modeling/circuits/test_circuit_cardinality.py index 72198f37b..d84bcd750 100644 --- a/tests/modules/applications/qml/generative_modeling/circuits/test_CircuitCardinality.py +++ b/tests/modules/applications/qml/generative_modeling/circuits/test_circuit_cardinality.py @@ -1,6 +1,6 @@ import unittest -from modules.applications.qml.generative_modeling.circuits.CircuitCardinality import CircuitCardinality +from modules.applications.qml.generative_modeling.circuits.circuit_cardinality import CircuitCardinality class TestCircuitCardinality(unittest.TestCase): diff --git a/tests/modules/applications/qml/generative_modeling/circuits/test_CircuitCopula.py b/tests/modules/applications/qml/generative_modeling/circuits/test_circuit_copula.py similarity index 97% rename from tests/modules/applications/qml/generative_modeling/circuits/test_CircuitCopula.py rename to tests/modules/applications/qml/generative_modeling/circuits/test_circuit_copula.py index 66744a07f..9f0c17ffa 100644 --- a/tests/modules/applications/qml/generative_modeling/circuits/test_CircuitCopula.py +++ b/tests/modules/applications/qml/generative_modeling/circuits/test_circuit_copula.py @@ -1,6 +1,6 @@ import unittest -from modules.applications.qml.generative_modeling.circuits.CircuitCopula import CircuitCopula +from modules.applications.qml.generative_modeling.circuits.circuit_copula import CircuitCopula class TestCircuitCopula(unittest.TestCase): diff --git a/tests/modules/applications/qml/generative_modeling/circuits/test_CircuitStandard.py b/tests/modules/applications/qml/generative_modeling/circuits/test_circuit_standard.py similarity index 97% rename from tests/modules/applications/qml/generative_modeling/circuits/test_CircuitStandard.py rename to tests/modules/applications/qml/generative_modeling/circuits/test_circuit_standard.py index 9a285ce66..b9f8f4dd4 100644 --- a/tests/modules/applications/qml/generative_modeling/circuits/test_CircuitStandard.py +++ b/tests/modules/applications/qml/generative_modeling/circuits/test_circuit_standard.py @@ -1,6 +1,6 @@ import unittest -from modules.applications.qml.generative_modeling.circuits.CircuitStandard import CircuitStandard +from modules.applications.qml.generative_modeling.circuits.circuit_standard import CircuitStandard class TestCircuitStandard(unittest.TestCase): diff --git a/tests/modules/applications/qml/generative_modeling/data/data_handler/test_ContinuousData.py b/tests/modules/applications/qml/generative_modeling/data/data_handler/test_continuous_data.py similarity index 97% rename from tests/modules/applications/qml/generative_modeling/data/data_handler/test_ContinuousData.py rename to tests/modules/applications/qml/generative_modeling/data/data_handler/test_continuous_data.py index e0fc19944..2f4f2a858 100644 --- a/tests/modules/applications/qml/generative_modeling/data/data_handler/test_ContinuousData.py +++ b/tests/modules/applications/qml/generative_modeling/data/data_handler/test_continuous_data.py @@ -3,7 +3,7 @@ import numpy as np -from modules.applications.qml.generative_modeling.data.data_handler.ContinuousData import ContinuousData +from modules.applications.qml.generative_modeling.data.data_handler.continuous_data import ContinuousData class TestContinuousData(unittest.TestCase): @@ -51,10 +51,10 @@ def test_get_default_submodule(self): self.data_handler.get_default_submodule("InvalidSubmodule") @patch( - "modules.applications.qml.generative_modeling.data.data_handler.ContinuousData.pkg_resources.resource_filename" + "modules.applications.qml.generative_modeling.data.data_handler.continuous_data.pkg_resources.resource_filename" ) @patch( - "modules.applications.qml.generative_modeling.data.data_handler.ContinuousData.np.load" + "modules.applications.qml.generative_modeling.data.data_handler.continuous_data.np.load" ) def test_data_load(self, mock_np_load, mock_resource_filename): mock_resource_filename.return_value = "/mock/path/X_2D.npy" diff --git a/tests/modules/applications/qml/generative_modeling/data/data_handler/test_DiscreteData.py b/tests/modules/applications/qml/generative_modeling/data/data_handler/test_discrete_data.py similarity index 92% rename from tests/modules/applications/qml/generative_modeling/data/data_handler/test_DiscreteData.py rename to tests/modules/applications/qml/generative_modeling/data/data_handler/test_discrete_data.py index dce76dbb5..c48975f9c 100644 --- a/tests/modules/applications/qml/generative_modeling/data/data_handler/test_DiscreteData.py +++ b/tests/modules/applications/qml/generative_modeling/data/data_handler/test_discrete_data.py @@ -3,9 +3,9 @@ import numpy as np -from modules.applications.qml.generative_modeling.data.data_handler.DiscreteData import DiscreteData -from modules.applications.qml.generative_modeling.circuits.CircuitCardinality import CircuitCardinality -from modules.applications.qml.generative_modeling.metrics.MetricsGeneralization import MetricsGeneralization +from modules.applications.qml.generative_modeling.data.data_handler.discrete_data import DiscreteData +from modules.applications.qml.generative_modeling.circuits.circuit_cardinality import CircuitCardinality +from modules.applications.qml.generative_modeling.metrics.metrics_generalization import MetricsGeneralization class TestDiscreteData(unittest.TestCase): diff --git a/tests/modules/applications/qml/generative_modeling/mappings/test_LibraryQiskit.py b/tests/modules/applications/qml/generative_modeling/mappings/test_LibraryQiskit.py deleted file mode 100644 index fb1d234ab..000000000 --- a/tests/modules/applications/qml/generative_modeling/mappings/test_LibraryQiskit.py +++ /dev/null @@ -1,146 +0,0 @@ -import unittest -from unittest.mock import MagicMock, patch - -from qiskit import QuantumCircuit -from qiskit_aer import AerSimulator - -from modules.applications.qml.generative_modeling.mappings.LibraryQiskit import LibraryQiskit -from modules.applications.qml.generative_modeling.training.QCBM import QCBM -from modules.applications.qml.generative_modeling.training.QGAN import QGAN -from modules.applications.qml.generative_modeling.training.Inference import Inference - - -class TestLibraryQiskit(unittest.TestCase): - - @classmethod - def setUpClass(cls): - cls.library_instance = LibraryQiskit() - - def test_initialization(self): - self.assertEqual(self.library_instance.name, "LibraryQiskit") - self.assertEqual(self.library_instance.submodule_options, ["QCBM", "QGAN", "Inference"]) - - def test_get_requirements(self): - requirements = self.library_instance.get_requirements() - expected_requirements = [ - {"name": "qiskit", "version": "1.3.0"}, - {"name": "numpy", "version": "1.26.4"} - ] - self.assertEqual(requirements, expected_requirements) - - def test_get_parameter_options(self): - parameter_options = self.library_instance.get_parameter_options() - expected_options = { - "backend": { - "values": ["aer_statevector_simulator_gpu", "aer_statevector_simulator_cpu", - "cusvaer_simulator (only available in cuQuantum appliance)", "aer_simulator_gpu", - "aer_simulator_cpu", "ionQ_Harmony", "Amazon_SV1", "ibm_brisbane IBM Quantum Platform"], - "description": "Which backend do you want to use? (aer_statevector_simulator uses the measurement " - "probability vector, the others are shot based)" - }, - "n_shots": { - "values": [100, 1000, 10000, 1000000], - "description": "How many shots do you want use for estimating the PMF of the model? " - "(If the aer_statevector_simulator selected, only relevant for studying generalization)" - } - } - self.assertEqual(parameter_options, expected_options) - - def test_get_default_submodule(self): - submodule = self.library_instance.get_default_submodule("QCBM") - self.assertIsInstance(submodule, QCBM) - - submodule = self.library_instance.get_default_submodule("QGAN") - self.assertIsInstance(submodule, QGAN) - - submodule = self.library_instance.get_default_submodule("Inference") - self.assertIsInstance(submodule, Inference) - - with self.assertRaises(NotImplementedError): - self.library_instance.get_default_submodule("InvalidSubmodule") - - def test_sequence_to_circuit(self): - input_data = { - "n_qubits": 2, - "gate_sequence": [ - ["Hadamard", [0]], - ["CNOT", [0, 1]], - ["RX", [0]], - ["RY", [1]], - ["RXX", [0, 1]] - ] - } - - output = self.library_instance.sequence_to_circuit(input_data) - - self.assertIn("circuit", output) - self.assertIsInstance(output["circuit"], QuantumCircuit) - self.assertIn("n_params", output) - self.assertEqual(output["n_params"], 3) # RX, RY, RXX need 3 parameters - - def test_select_backend(self): - with patch("qiskit_aer.Aer.get_backend", return_value=AerSimulator()) as mock_backend: - backend = self.library_instance.select_backend("aer_simulator_cpu", 2) - mock_backend.assert_called_once_with("aer_simulator") - self.assertIsInstance(backend, AerSimulator) - - with self.assertRaises(NotImplementedError): - self.library_instance.select_backend("unknown_backend", 2) - - @patch("qiskit_aer.Aer.get_backend") - def test_aer_simulator_gpu(self, mock_get_backend): - mock_backend = MagicMock() - mock_get_backend.return_value = mock_backend - - backend = self.library_instance.select_backend("aer_simulator_gpu", 4) - mock_get_backend.assert_called_once_with("aer_simulator") - mock_backend.set_options.assert_called_once_with(device="GPU") - self.assertEqual(backend, mock_backend) - - @patch("qiskit_aer.Aer.get_backend") - def test_aer_simulator_cpu(self, mock_get_backend): - mock_backend = MagicMock() - mock_get_backend.return_value = mock_backend - - backend = self.library_instance.select_backend("aer_simulator_cpu", 4) - mock_get_backend.assert_called_once_with("aer_simulator") - mock_backend.set_options.assert_called_once_with(device="CPU") - self.assertEqual(backend, mock_backend) - - @patch("qiskit_aer.Aer.get_backend") - def test_aer_statevector_simulator_gpu(self, mock_get_backend): - mock_backend = MagicMock() - mock_get_backend.return_value = mock_backend - - backend = self.library_instance.select_backend("aer_statevector_simulator_gpu", 4) - mock_get_backend.assert_called_once_with("statevector_simulator") - mock_backend.set_options.assert_called_once_with(device="GPU") - self.assertEqual(backend, mock_backend) - - @patch("qiskit_aer.Aer.get_backend") - def test_aer_statevector_simulator_cpu(self, mock_get_backend): - mock_backend = MagicMock() - mock_get_backend.return_value = mock_backend - - backend = self.library_instance.select_backend("aer_statevector_simulator_cpu", 4) - mock_get_backend.assert_called_once_with("statevector_simulator") - mock_backend.set_options.assert_called_once_with(device="CPU") - self.assertEqual(backend, mock_backend) - - def test_invalid_configuration(self): - with self.assertRaises(NotImplementedError) as context: - self.library_instance.select_backend("invalid.backend", 4) - self.assertIn("Device Configuration invalid.backend not implemented", str(context.exception)) - - def test_get_execute_circuit(self): - circuit = QuantumCircuit(2) - circuit.h(0) - backend = AerSimulator() - config_dict = {"n_shots": 100} - - execute_circuit, transpiled_circuit = self.library_instance.get_execute_circuit( - circuit, backend, "aer_simulator_cpu", config_dict - ) - - self.assertIsNotNone(execute_circuit) - self.assertIsInstance(transpiled_circuit, QuantumCircuit) diff --git a/tests/modules/applications/qml/generative_modeling/mappings/test_CustomQiskitNoisyBackend.py b/tests/modules/applications/qml/generative_modeling/mappings/test_custom_qiskit_noisy_backend.py similarity index 83% rename from tests/modules/applications/qml/generative_modeling/mappings/test_CustomQiskitNoisyBackend.py rename to tests/modules/applications/qml/generative_modeling/mappings/test_custom_qiskit_noisy_backend.py index 33b9e41e7..4c3216e7a 100644 --- a/tests/modules/applications/qml/generative_modeling/mappings/test_CustomQiskitNoisyBackend.py +++ b/tests/modules/applications/qml/generative_modeling/mappings/test_custom_qiskit_noisy_backend.py @@ -1,13 +1,12 @@ import unittest -from unittest.mock import ANY, MagicMock, patch - +from unittest.mock import MagicMock, patch import numpy as np -from qiskit import QuantumCircuit -from qiskit.transpiler import CouplingMap +from qiskit.circuit import QuantumCircuit, Parameter from qiskit_aer import AerSimulator from qiskit_aer.noise import NoiseModel +from qiskit.transpiler import CouplingMap, Layout -from modules.applications.qml.generative_modeling.mappings.CustomQiskitNoisyBackend import CustomQiskitNoisyBackend +from modules.applications.qml.generative_modeling.mappings.custom_qiskit_noisy_backend import CustomQiskitNoisyBackend class TestCustomQiskitNoisyBackend(unittest.TestCase): @@ -66,7 +65,7 @@ def test_sequence_to_circuit(self): self.assertIsInstance(output_data["circuit"], QuantumCircuit) self.assertEqual(output_data["n_params"], 1) # One parameterized gate in the sequence - @patch("modules.applications.qml.generative_modeling.mappings.CustomQiskitNoisyBackend.Aer.get_backend") + @patch("modules.applications.qml.generative_modeling.mappings.custom_qiskit_noisy_backend.Aer.get_backend") def test_select_backend(self, mock_get_backend): # Mock the backend and its set_options method mock_backend = MagicMock() @@ -94,22 +93,21 @@ def test_select_backend(self, mock_get_backend): self.backend_instance.select_backend("unknown_backend", 3) @patch( - "modules.applications.qml.generative_modeling.mappings.CustomQiskitNoisyBackend.Layout" + "modules.applications.qml.generative_modeling.mappings.custom_qiskit_noisy_backend.Layout" ) @patch( - "modules.applications.qml.generative_modeling.mappings.CustomQiskitNoisyBackend.PassManager" + "modules.applications.qml.generative_modeling.mappings.custom_qiskit_noisy_backend.PassManager" ) @patch( - "modules.applications.qml.generative_modeling.mappings.CustomQiskitNoisyBackend.transpile" + "modules.applications.qml.generative_modeling.mappings.custom_qiskit_noisy_backend.transpile" ) @patch( - "modules.applications.qml.generative_modeling.mappings.CustomQiskitNoisyBackend.AerSimulator" + "modules.applications.qml.generative_modeling.mappings.custom_qiskit_noisy_backend.AerSimulator" ) @patch( - "modules.applications.qml.generative_modeling.mappings.CustomQiskitNoisyBackend." + "modules.applications.qml.generative_modeling.mappings.custom_qiskit_noisy_backend." "CustomQiskitNoisyBackend.decompile_noisy_config" ) - # pylint: disable=R0917 def test_get_execute_circuit(self, mock_decompile_noisy_config, mock_aer_simulator, mock_transpile, mock_pass_manager, mock_layout): # Mock Configurations @@ -117,16 +115,19 @@ def test_get_execute_circuit(self, mock_decompile_noisy_config, mock_aer_simulat mock_decompile_noisy_config.return_value = mock_backend mock_pass_manager.return_value.run.return_value = "processed_circuit" - # Mock Circuit for Transpilation - mock_transpiled_circuit = MagicMock(spec=QuantumCircuit) - mock_transpiled_circuit.count_ops.return_value = {"h": 3, "cx": 2} - mock_transpiled_circuit.assign_parameters = MagicMock() - mock_transpile.return_value = mock_transpiled_circuit + # Mock Layout + mock_layout.return_value = MagicMock(spec=Layout) - # Mock Circuit - mock_circuit = MagicMock(spec=QuantumCircuit) - mock_circuit.num_qubits = 3 - mock_circuit.count_ops.return_value = {"h": 3, "cx": 2} + # Create a real QuantumCircuit with parameterized gates + real_circuit = QuantumCircuit(3) + param_x = Parameter("x_000") + param_y = Parameter("x_001") + real_circuit.rx(param_x, 0) + real_circuit.ry(param_y, 1) + real_circuit.measure_all() + + mock_transpiled_circuit = real_circuit.copy() + mock_transpile.return_value = mock_transpiled_circuit # Mock Backend Run mock_job = MagicMock() @@ -148,18 +149,17 @@ def test_get_execute_circuit(self, mock_decompile_noisy_config, mock_aer_simulat # Call the method execute_circuit, circuit_transpiled = self.backend_instance.get_execute_circuit( - circuit=mock_circuit, + circuit=real_circuit, backend=mock_backend, config="aer_simulator_cpu", config_dict=config_dict, ) # Assertions - self.assertEqual(circuit_transpiled, mock_transpiled_circuit) self.assertTrue(callable(execute_circuit)) - # Mock Solutions - solutions = [{"param_0": 0.5}, {"param_0": 1.0}] + # Mock Solutions with correct parameter names + solutions = [{param_x: 0.5, param_y: 0.7}, {param_x: 1.0, param_y: 1.2}] pmfs, samples = execute_circuit(solutions) # Assertions on returned values @@ -168,28 +168,20 @@ def test_get_execute_circuit(self, mock_decompile_noisy_config, mock_aer_simulat self.assertEqual(pmfs.shape[0], len(solutions)) self.assertEqual(samples.shape[0], len(solutions)) - # Check calls to mocks - mock_decompile_noisy_config.assert_called_once_with(config_dict, 3) - mock_pass_manager.return_value.run.assert_called_once_with(mock_circuit) - mock_transpile.assert_called_once_with( - "processed_circuit", backend=mock_backend, optimization_level=2, seed_transpiler=42, coupling_map=ANY - ) - mock_backend.run.assert_called_once() - @patch( - "modules.applications.qml.generative_modeling.mappings.CustomQiskitNoisyBackend." + "modules.applications.qml.generative_modeling.mappings.custom_qiskit_noisy_backend." "CustomQiskitNoisyBackend.build_noise_model" ) @patch( - "modules.applications.qml.generative_modeling.mappings.CustomQiskitNoisyBackend." + "modules.applications.qml.generative_modeling.mappings.custom_qiskit_noisy_backend." "CustomQiskitNoisyBackend.get_coupling_map" ) @patch( - "modules.applications.qml.generative_modeling.mappings.CustomQiskitNoisyBackend." + "modules.applications.qml.generative_modeling.mappings.custom_qiskit_noisy_backend." "Aer.get_backend" ) @patch( - "modules.applications.qml.generative_modeling.mappings.CustomQiskitNoisyBackend." + "modules.applications.qml.generative_modeling.mappings.custom_qiskit_noisy_backend." "CustomQiskitNoisyBackend.log_backend_options" ) def test_decompile_noisy_config(self, mock_log_backend_options, mock_get_backend, @@ -216,7 +208,11 @@ def test_decompile_noisy_config(self, mock_log_backend_options, mock_get_backend config_dict = { "backend": "aer_simulator_cpu", "simulation_method": "statevector", - "noise_configuration": "No noise" + "noise_configuration": "No noise", + "custom_readout_error": 0.0, + "two_qubit_depolarizing_errors": 0.0, + "one_qubit_depolarizing_errors": 0.0, + "qubit_layout": "linear" } num_qubits = 4 @@ -225,7 +221,6 @@ def test_decompile_noisy_config(self, mock_log_backend_options, mock_get_backend self.assertEqual(backend.name, "aer_simulator_statevector", "Expected default AerSimulator backend") mock_get_backend.assert_called_once_with("aer_simulator") mock_backend.set_options.assert_called_once_with(device=device, method=simulation_method) - mock_log_backend_options.assert_called_once_with(mock_backend) # Reset mocks for the next test case mock_get_backend.reset_mock() @@ -238,8 +233,6 @@ def test_decompile_noisy_config(self, mock_log_backend_options, mock_get_backend # Assertions for custom backend self.assertIsInstance(backend, AerSimulator, "Expected AerSimulator instance for custom configuration") - mock_build_noise_model.assert_called_once_with(config_dict) - mock_get_coupling_map.assert_called_once_with(config_dict, num_qubits) def test_build_noise_model(self): config_dict = { @@ -288,7 +281,7 @@ def test_get_transpile_routine(self): self.assertEqual(self.backend_instance.get_transpile_routine(2), 2) self.assertEqual(self.backend_instance.get_transpile_routine(5), 1) # Invalid config defaults to 1 - @patch("modules.applications.qml.generative_modeling.mappings.CustomQiskitNoisyBackend.noise.depolarizing_error") + @patch("modules.applications.qml.generative_modeling.mappings.custom_qiskit_noisy_backend.noise.depolarizing_error") def test_add_quantum_errors(self, mock_depolarizing_error): # Mock noise model and depolarizing errors mock_noise_model = MagicMock(spec=NoiseModel) diff --git a/tests/modules/applications/qml/generative_modeling/mappings/test_LibraryPennylane.py b/tests/modules/applications/qml/generative_modeling/mappings/test_library_pennylane.py similarity index 93% rename from tests/modules/applications/qml/generative_modeling/mappings/test_LibraryPennylane.py rename to tests/modules/applications/qml/generative_modeling/mappings/test_library_pennylane.py index 4e330669a..e82737b3b 100644 --- a/tests/modules/applications/qml/generative_modeling/mappings/test_LibraryPennylane.py +++ b/tests/modules/applications/qml/generative_modeling/mappings/test_library_pennylane.py @@ -3,10 +3,10 @@ import numpy as np -from modules.applications.qml.generative_modeling.mappings.LibraryPennylane import LibraryPennylane -from modules.applications.qml.generative_modeling.training.QCBM import QCBM -from modules.applications.qml.generative_modeling.training.QGAN import QGAN -from modules.applications.qml.generative_modeling.training.Inference import Inference +from modules.applications.qml.generative_modeling.mappings.library_pennylane import LibraryPennylane +from modules.applications.qml.generative_modeling.training.qcbm import QCBM +from modules.applications.qml.generative_modeling.training.qgan import QGAN +from modules.applications.qml.generative_modeling.training.inference import Inference class TestLibraryPennylane(unittest.TestCase): diff --git a/tests/modules/applications/qml/generative_modeling/mappings/test_library_qiskit.py b/tests/modules/applications/qml/generative_modeling/mappings/test_library_qiskit.py new file mode 100644 index 000000000..c67a8dc33 --- /dev/null +++ b/tests/modules/applications/qml/generative_modeling/mappings/test_library_qiskit.py @@ -0,0 +1,242 @@ +import unittest +from unittest.mock import patch, MagicMock +from qiskit import QuantumCircuit +import numpy as np +from qiskit_aer import AerSimulator + +from modules.applications.qml.generative_modeling.mappings.library_qiskit import LibraryQiskit +from modules.applications.qml.generative_modeling.training.qcbm import QCBM +from modules.applications.qml.generative_modeling.training.qgan import QGAN +from modules.applications.qml.generative_modeling.training.inference import Inference + + +class TestLibraryQiskit(unittest.TestCase): + + @classmethod + def setUpClass(cls): + cls.library_instance = LibraryQiskit() + + def test_initialization(self): + self.assertEqual(self.library_instance.name, "LibraryQiskit") + self.assertEqual(self.library_instance.submodule_options, ["QCBM", "QGAN", "Inference"]) + + def test_get_requirements(self): + requirements = self.library_instance.get_requirements() + expected_requirements = [ + {"name": "qiskit", "version": "1.3.0"}, + {"name": "numpy", "version": "1.26.4"}, + {"name": "qiskit_aer", "version": "0.15.1"} + ] + self.assertEqual(requirements, expected_requirements) + + def test_get_parameter_options(self): + parameter_options = self.library_instance.get_parameter_options() + expected_options = { + "backend": { + "values": ["aer_statevector_simulator_gpu", "aer_statevector_simulator_cpu", + "cusvaer_simulator (only available in cuQuantum appliance)", "aer_simulator_gpu", + "aer_simulator_cpu", "ionQ_Harmony", "Amazon_SV1", "ibm_brisbane IBM Quantum Platform"], + "description": "Which backend do you want to use? (aer_statevector_simulator uses the measurement " + "probability vector, the others are shot based)" + }, + "n_shots": { + "values": [100, 1000, 10000, 1000000], + "description": "How many shots do you want use for estimating the PMF of the model? " + "(If the aer_statevector_simulator selected, only relevant for studying generalization)" + } + } + self.assertEqual(parameter_options, expected_options) + + def test_get_default_submodule(self): + submodule = self.library_instance.get_default_submodule("QCBM") + self.assertIsInstance(submodule, QCBM) + + submodule = self.library_instance.get_default_submodule("QGAN") + self.assertIsInstance(submodule, QGAN) + + submodule = self.library_instance.get_default_submodule("Inference") + self.assertIsInstance(submodule, Inference) + + with self.assertRaises(NotImplementedError): + self.library_instance.get_default_submodule("InvalidSubmodule") + + def test_sequence_to_circuit(self): + input_data = { + "n_qubits": 2, + "gate_sequence": [ + ["Hadamard", [0]], + ["CNOT", [0, 1]], + ["RX", [0]], + ["RY", [1]], + ["RXX", [0, 1]] + ] + } + + output = self.library_instance.sequence_to_circuit(input_data) + + self.assertIn("circuit", output) + self.assertIsInstance(output["circuit"], QuantumCircuit) + self.assertIn("n_params", output) + self.assertEqual(output["n_params"], 3) # RX, RY, RXX need 3 parameters + + # These tests are currently commented out because implementing test cases for the + # cusvaer simulator is challenging due to the complexity of mocking certain + # behaviors of the `cusvaer`-enabled backend. We plan to implement these tests + # in the future once we have resolved these issues. + # @patch("modules.applications.qml.generative_modeling.mappings.library_qiskit.select_backend.cusvaer") + # @patch("qiskit_aer.Aer.get_backend") + # def test_cusvaer_simulator(self, mock_aer_simulator, mock_cusvaer): + # mock_backend = MagicMock() + # mock_aer_simulator.return_value = mock_backend + + # backend = self.library_instance.select_backend( + # "cusvaer_simulator (only available in cuQuantum appliance)", 5 + # ) + # self.assertEqual(backend, mock_backend) + # mock_aer_simulator.assert_called_once_with( + # method="statevector", + # device="GPU", + # cusvaer_enable=True, + # noise_model=None, + # cusvaer_p2p_device_bits=3, + # cusvaer_comm_plugin_type=mock_cusvaer.CommPluginType.MPI_AUTO, + # cusvaer_comm_plugin_soname="libmpi.so", + # ) + + @patch("qiskit_aer.Aer.get_backend") + def test_aer_simulator_gpu(self, mock_get_backend): + mock_backend = MagicMock() + mock_get_backend.return_value = mock_backend + + backend = self.library_instance.select_backend("aer_simulator_gpu", 4) + mock_get_backend.assert_called_once_with("aer_simulator") + mock_backend.set_options.assert_called_once_with(device="GPU") + self.assertEqual(backend, mock_backend) + + @patch("qiskit_aer.Aer.get_backend") + def test_aer_simulator_cpu(self, mock_get_backend): + mock_backend = MagicMock() + mock_get_backend.return_value = mock_backend + + backend = self.library_instance.select_backend("aer_simulator_cpu", 4) + mock_get_backend.assert_called_once_with("aer_simulator") + mock_backend.set_options.assert_called_once_with(device="CPU") + self.assertEqual(backend, mock_backend) + + @patch("qiskit_aer.Aer.get_backend") + def test_aer_statevector_simulator_gpu(self, mock_get_backend): + mock_backend = MagicMock() + mock_get_backend.return_value = mock_backend + + backend = self.library_instance.select_backend("aer_statevector_simulator_gpu", 4) + mock_get_backend.assert_called_once_with("statevector_simulator") + mock_backend.set_options.assert_called_once_with(device="GPU") + self.assertEqual(backend, mock_backend) + + @patch("qiskit_aer.Aer.get_backend") + def test_aer_statevector_simulator_cpu(self, mock_get_backend): + mock_backend = MagicMock() + mock_get_backend.return_value = mock_backend + + backend = self.library_instance.select_backend("aer_statevector_simulator_cpu", 4) + mock_get_backend.assert_called_once_with("statevector_simulator") + mock_backend.set_options.assert_called_once_with(device="CPU") + self.assertEqual(backend, mock_backend) + + # The following tests are commented out because: + # - The `AWSBraketBackend` and `AWSBraketProvider` are complex to mock in the current setup. + # - Additional setup or dependency resolution is required for testing with AWS Braket devices (e.g., SV1 or IonQ Harmony). + # def test_amazon_sv1(self): + # from qiskit_braket_provider import AWSBraketBackend, AWSBraketProvider + # from modules.devices.braket.sv1 import SV1 + + # # Create a mock device wrapper and backend + # device_wrapper = SV1("SV1", "arn:aws:braket:::device/quantum-simulator/amazon/sv1") + # backend = AWSBraketBackend( + # device=device_wrapper.device, + # provider=AWSBraketProvider(), + # name=device_wrapper.device.name, + # description=f"AWS Device: {device_wrapper.device.provider_name} {device_wrapper.device.name}.", + # online_date=device_wrapper.device.properties.service.updatedAt, + # backend_version="2", + # ) + + # # Assert that the backend behaves as expected + # self.assertIsNotNone(backend) + # self.assertEqual(backend.name, device_wrapper.device.name) + + # @patch("modules.devices.braket.ionq.Ionq") + # @patch("qiskit_braket_provider.AWSBraketBackend") + # def test_ionq_harmony(self, mock_aws_braket_backend, mock_ionq): + # mock_device_wrapper = MagicMock() + # mock_ionq.return_value = mock_device_wrapper + + # backend = self.library_instance.select_backend("ionQ_Harmony", 4) + # mock_aws_braket_backend.assert_called_once() + # self.assertEqual(backend, mock_aws_braket_backend.return_value) + + def test_invalid_configuration(self): + with self.assertRaises(NotImplementedError) as context: + self.library_instance.select_backend("invalid.backend", 4) + self.assertIn("Device Configuration invalid.backend not implemented", str(context.exception)) + + # These tests are commented out because: + # - The complexity of mocking the behavior of Qiskit components (e.g., `transpile`, `Statevector`, and `AerSimulator`) + # makes it challenging to implement these tests in the current setup. + # - The dependency on specific Qiskit modules and features requires more robust mocking strategies. + # - We plan to revisit these tests in the future. + # @patch("qiskit.transpiler.transpile") + # @patch("qiskit.quantum_info.Statevector") + # def test_aer_statevector_simulator(self, mock_statevector, mock_transpile): + # mock_circuit = MagicMock(spec=QuantumCircuit) + # mock_transpiled_circuit = MagicMock(spec=QuantumCircuit) + # mock_transpile.return_value = mock_transpiled_circuit + # mock_statevector.return_value.probabilities.return_value = np.array([0.25, 0.75]) + + # # Config + # config = "aer_statevector_simulator_gpu" + # config_dict = {"n_shots": 100} + # backend = MagicMock() + + # execute_circuit, transpiled_circuit = self.library_instance.get_execute_circuit( + # mock_circuit, backend, config, config_dict + # ) + + # self.assertEqual(transpiled_circuit, mock_transpiled_circuit) + # solutions = [np.array([0.1, 0.9]), np.array([0.8, 0.2])] + # pmfs, samples = execute_circuit(solutions) + + # # Validate the outputs + # self.assertIsInstance(pmfs, np.ndarray) + # self.assertIsNone(samples) + # self.assertEqual(pmfs.shape, (2, 2)) + # np.testing.assert_array_equal(pmfs[0], [0.25, 0.75]) + + # @patch("qiskit.transpile") + # @patch("qiskit_aer.AerSimulator.run") + # def test_aer_simulator(self, mock_run, mock_transpile): + # mock_circuit = MagicMock(spec=QuantumCircuit) + # mock_transpiled_circuit = MagicMock(spec=QuantumCircuit) + # mock_transpile.return_value = mock_transpiled_circuit + # mock_job = MagicMock() + # mock_job.result.return_value.get_counts.return_value.int_outcomes.return_value = {0: 10, 1: 20} + # mock_run.return_value = mock_job + + # # Config + # mock_backend = MagicMock(spec=AerSimulator) + # mock_backend.version = 2 + # config = "aer_simulator_gpu" + # config_dict = {"n_shots": 100} + + # execute_circuit, transpiled_circuit = self.library_instance.get_execute_circuit( + # mock_circuit, mock_backend, config, config_dict + # ) + + # self.assertEqual(transpiled_circuit, mock_transpiled_circuit) + # solutions = [np.array([0.1, 0.9]), np.array([0.8, 0.2])] + # pmfs, samples = execute_circuit(solutions) + + # self.assertIsInstance(pmfs, np.ndarray) + # self.assertIsInstance(samples, np.ndarray) + # self.assertEqual(pmfs.shape, (2, 2)) + # self.assertEqual(samples.shape, (2, 2)) diff --git a/tests/modules/applications/qml/generative_modeling/mappings/test_PresetQiskitNoisyBackend.py b/tests/modules/applications/qml/generative_modeling/mappings/test_preset_qiskit_noisy_backend.py similarity index 95% rename from tests/modules/applications/qml/generative_modeling/mappings/test_PresetQiskitNoisyBackend.py rename to tests/modules/applications/qml/generative_modeling/mappings/test_preset_qiskit_noisy_backend.py index 172a1f583..be74fd9a1 100644 --- a/tests/modules/applications/qml/generative_modeling/mappings/test_PresetQiskitNoisyBackend.py +++ b/tests/modules/applications/qml/generative_modeling/mappings/test_preset_qiskit_noisy_backend.py @@ -1,14 +1,11 @@ -import pickle import unittest -from unittest.mock import MagicMock, patch - +from unittest.mock import patch, MagicMock import numpy as np +import pickle from qiskit import QuantumCircuit from qiskit_aer import AerSimulator -from modules.applications.qml.generative_modeling.training.QCBM import QCBM -from modules.applications.qml.generative_modeling.training.Inference import Inference -from modules.applications.qml.generative_modeling.mappings.PresetQiskitNoisyBackend import PresetQiskitNoisyBackend +from modules.applications.qml.generative_modeling.mappings.preset_qiskit_noisy_backend import PresetQiskitNoisyBackend class TestPresetQiskitNoisyBackend(unittest.TestCase): @@ -34,6 +31,8 @@ def test_get_requirements(self): self.assertEqual(requirements, expected_requirements) def test_get_default_submodule(self): + from modules.applications.qml.generative_modeling.training.qcbm import QCBM + from modules.applications.qml.generative_modeling.training.inference import Inference submodule = self.backend_instance.get_default_submodule("QCBM") self.assertIsInstance(submodule, QCBM) @@ -153,7 +152,7 @@ def test_get_transpile_routine(self): "qiskit_aer.Aer.get_backend" ) @patch( - "modules.applications.qml.generative_modeling.mappings.PresetQiskitNoisyBackend." + "modules.applications.qml.generative_modeling.mappings.preset_qiskit_noisy_backend." "PresetQiskitNoisyBackend.get_FakeBackend" ) def test_decompile_noisy_config(self, mock_get_fake_backend, mock_get_backend): @@ -222,7 +221,7 @@ def test_log_backend_info(self, mock_logging): @patch("qiskit_aer.noise.NoiseModel.from_backend") @patch("qiskit_aer.AerSimulator.from_backend") @patch("qiskit_aer.Aer.get_backend") - @patch("modules.applications.qml.generative_modeling.mappings.PresetQiskitNoisyBackend.FakeProviderForBackendV2") + @patch("modules.applications.qml.generative_modeling.mappings.preset_qiskit_noisy_backend.FakeProviderForBackendV2") def test_get_FakeBackend(self, mock_provider, mock_aer_get_backend, mock_simulator_from_backend, mock_noise_model): mock_backend = MagicMock() mock_backend.num_qubits = 5 diff --git a/tests/modules/applications/qml/generative_modeling/metrics/test_MetricsGeneralization.py b/tests/modules/applications/qml/generative_modeling/metrics/test_metrics_generalization.py similarity index 96% rename from tests/modules/applications/qml/generative_modeling/metrics/test_MetricsGeneralization.py rename to tests/modules/applications/qml/generative_modeling/metrics/test_metrics_generalization.py index e2e21c006..60ee26ed4 100644 --- a/tests/modules/applications/qml/generative_modeling/metrics/test_MetricsGeneralization.py +++ b/tests/modules/applications/qml/generative_modeling/metrics/test_metrics_generalization.py @@ -3,7 +3,7 @@ import numpy as np -from modules.applications.qml.generative_modeling.metrics.MetricsGeneralization import MetricsGeneralization +from modules.applications.qml.generative_modeling.metrics.metrics_generalization import MetricsGeneralization class TestMetricsGeneralization(unittest.TestCase): diff --git a/tests/modules/applications/qml/generative_modeling/training/test_Inference.py b/tests/modules/applications/qml/generative_modeling/training/test_Inference.py index 65ca43a81..a900020ec 100644 --- a/tests/modules/applications/qml/generative_modeling/training/test_Inference.py +++ b/tests/modules/applications/qml/generative_modeling/training/test_Inference.py @@ -3,7 +3,7 @@ import numpy as np -from modules.applications.qml.generative_modeling.training.Inference import Inference +from modules.applications.qml.generative_modeling.training.inference import Inference class TestInference(unittest.TestCase): diff --git a/tests/modules/applications/qml/generative_modeling/training/test_QCBM.py b/tests/modules/applications/qml/generative_modeling/training/test_QCBM.py deleted file mode 100644 index ea74a5425..000000000 --- a/tests/modules/applications/qml/generative_modeling/training/test_QCBM.py +++ /dev/null @@ -1,131 +0,0 @@ -import unittest -from unittest.mock import MagicMock, patch - -import matplotlib.pyplot as plt -import numpy as np - -from modules.applications.qml.generative_modeling.training.QCBM import QCBM - - -class TestQCBM(unittest.TestCase): - - @classmethod - def setUpClass(cls): - cls.qcbm_instance = QCBM() - - def test_get_requirements(self): - requirements = self.qcbm_instance.get_requirements() - expected_requirements = [ - {"name": "numpy", "version": "1.26.4"}, - {"name": "cma", "version": "4.0.0"}, - {"name": "matplotlib", "version": "3.9.3"}, - {"name": "tensorboard", "version": "2.18.0"}, - {"name": "tensorboardX", "version": "2.6.2.2"} - ] - self.assertEqual(requirements, expected_requirements) - - def test_get_parameter_options(self): - parameter_options = self.qcbm_instance.get_parameter_options() - self.assertIn("population_size", parameter_options) - self.assertIn("max_evaluations", parameter_options) - self.assertIn("sigma", parameter_options) - self.assertIn("pretrained", parameter_options) - self.assertIn("loss", parameter_options) - - def test_get_default_submodule(self): - with self.assertRaises(ValueError): - self.qcbm_instance.get_default_submodule("AnyOption") - - @patch("numpy.random.rand") - @patch("modules.applications.qml.generative_modeling.training.QCBM.SummaryWriter") - def test_setup_training(self, mock_summary_writer, mock_rand): - # Mock inputs - mock_rand.return_value = np.array([0.5, 0.5, 0.5]) - mock_summary_writer.return_value = MagicMock() - input_data = { - "backend": "test_backend", - "n_qubits": 5, - "store_dir_iter": "./test_dir", - "n_params": 3 - } - config = { - "population_size": 5, - "max_evaluations": 100, - "sigma": 0.5, - "pretrained": "False", - "loss": "KL" - } - - x0, options = self.qcbm_instance.setup_training(input_data, config) - self.assertEqual(x0.shape, (3,)) - self.assertIn("bounds", options) - self.assertEqual(options["popsize"], 5) - - def test_start_training(self): - # Mock input data and configuration - input_data = { - "backend": "aer_simulator", - "n_qubits": 4, # 2^4 = 16 states - "store_dir_iter": "/tmp/test_qcbm", - "n_params": 16, - "generalization_metrics": MagicMock(), - "n_shots": 1000, - "histogram_train": np.full(16, 1 / 16), - "execute_circuit": MagicMock( - return_value=(np.tile(np.full(16, 1 / 16), (10, 1)), None) - ), - "dataset_name": "test_dataset", - } - - config = { - "loss": "KL", - "population_size": 10, - "max_evaluations": 1000, - "sigma": 0.5, - "pretrained": "False", - } - try: - result = self.qcbm_instance.start_training(input_data, config) - - # Validate results - self.assertIn("best_parameters", result, "Result should include 'best_parameters'.") - self.assertIn("KL", result, "Result should include 'KL'.") - self.assertGreater(len(result["best_parameters"]), 0, "Best parameters should not be empty.") - self.assertGreater(len(result["KL"]), 0, "KL values should not be empty.") - except ValueError as e: - # Print PMF and population details for debugging - print(f"PMF size: {len(input_data['execute_circuit'].return_value[0])}") - print(f"PMF sum: {np.sum(input_data['execute_circuit'].return_value[0], axis=1)}") - print(f"Population size: {config['population_size']}") - raise e - - def test_data_visualization(self): - # Mock generalization metrics - self.qcbm_instance.study_generalization = True - self.qcbm_instance.generalization_metrics = MagicMock() - self.qcbm_instance.generalization_metrics.n_shots = 1000 - - # Mock writer - self.qcbm_instance.writer = MagicMock() - - # Define target explicitly - self.qcbm_instance.target = np.array([0.1] * 16) - self.qcbm_instance.target[self.qcbm_instance.target == 0] = 1e-8 - - # Define `n_qubits` and `n_states_range` - n_qubits = 4 - self.qcbm_instance.n_states_range = np.arange(2 ** n_qubits) - - self.qcbm_instance.fig, self.qcbm_instance.ax = plt.subplots() - - loss_epoch = np.array([0.1, 0.2, 0.3]) - pmfs_model = np.array([[0.1] * 16]) - pmfs_model /= pmfs_model.sum(axis=1, keepdims=True) - samples = None - - best_pmf = self.qcbm_instance.data_visualization(loss_epoch, pmfs_model, samples, epoch=1) - - # Validate the results - self.assertIsNotNone(best_pmf, "Best PMF should not be None.") - self.qcbm_instance.writer.add_scalar.assert_called() - self.qcbm_instance.writer.add_figure.assert_called_with('grid_figure', self.qcbm_instance.fig, global_step=1) diff --git a/tests/modules/applications/qml/generative_modeling/training/test_inference.py b/tests/modules/applications/qml/generative_modeling/training/test_inference.py new file mode 100644 index 000000000..a900020ec --- /dev/null +++ b/tests/modules/applications/qml/generative_modeling/training/test_inference.py @@ -0,0 +1,74 @@ +import unittest +from unittest.mock import MagicMock, patch + +import numpy as np + +from modules.applications.qml.generative_modeling.training.inference import Inference + + +class TestInference(unittest.TestCase): + + @classmethod + def setUpClass(cls): + cls.inference_instance = Inference() + cls.mock_parameters = np.array([0.5, 0.2, 0.3]) + cls.config = {"pretrained": "mock_pretrained_file.npy"} + cls.input_data = { + "n_qubits": 3, + "histogram_train": np.array([0.1, 0.2, 0.3, 0.4]), + "execute_circuit": MagicMock(), + "n_shots": 1000, + } + + def test_get_requirements(self): + requirements = self.inference_instance.get_requirements() + expected_requirements = [{"name": "numpy", "version": "1.26.4"}] + self.assertEqual(requirements, expected_requirements) + + def test_get_parameter_options(self): + parameter_options = self.inference_instance.get_parameter_options() + expected_options = { + "pretrained": { + "values": [], + "custom_input": True, + "postproc": str, + "description": "Please provide the parameters of a pretrained model.", + } + } + self.assertEqual(parameter_options, expected_options) + + def test_get_default_submodule(self): + with self.assertRaises(ValueError): + self.inference_instance.get_default_submodule("any_option") + + @patch("numpy.load") + def test_start_training(self, mock_np_load): + # Mock np.load to return mock parameters + mock_np_load.return_value = self.mock_parameters + + # Update the execute_circuit mock to return a PMF with the correct size + n_states = 2 ** self.input_data["n_qubits"] + pmf_mock = np.full(n_states, 1 / n_states) + self.input_data["execute_circuit"].return_value = ([pmf_mock], None) + + result = self.inference_instance.start_training(self.input_data, self.config) + + # Validate the returned dictionary keys + self.assertIn("best_parameter", result, "'best_parameter' key is missing from the result.") + self.assertIn("inference", result, "'inference' key is missing from the result.") + self.assertIn("KL", result, "'KL' key is missing from the result.") + self.assertIn("best_sample", result, "'best_sample' key is missing from the result.") + + self.assertTrue(result["inference"], "The 'inference' flag should be True.") + + # Extract KL divergence + kl_values = result["KL"] + self.assertIsInstance(kl_values, list, "KL divergence values should be returned as a list.") + + self.assertTrue((result["best_sample"] >= 0).all(), "Best sample should contain non-negative integers.") + + best_parameter = result["best_parameter"] + np.testing.assert_array_equal( + best_parameter, + self.mock_parameters, + "Best parameter does not match the expected parameters.") diff --git a/tests/modules/applications/qml/generative_modeling/training/test_QGAN.py b/tests/modules/applications/qml/generative_modeling/training/test_qgan.py similarity index 98% rename from tests/modules/applications/qml/generative_modeling/training/test_QGAN.py rename to tests/modules/applications/qml/generative_modeling/training/test_qgan.py index 72076c9ec..11260b729 100644 --- a/tests/modules/applications/qml/generative_modeling/training/test_QGAN.py +++ b/tests/modules/applications/qml/generative_modeling/training/test_qgan.py @@ -4,7 +4,7 @@ import numpy as np import torch from torch.utils.data import DataLoader -from modules.applications.qml.generative_modeling.training.QGAN import QGAN, Discriminator, QuantumGenerator +from modules.applications.qml.generative_modeling.training.qgan import QGAN, Discriminator, QuantumGenerator class TestQGAN(unittest.TestCase): @@ -40,7 +40,7 @@ def test_get_requirements(self): requirements = self.qgan_instance.get_requirements() expected_requirements = [ {"name": "numpy", "version": "1.26.4"}, - {"name": "torch", "version": "2.5.1"}, + {"name": "torch", "version": "2.2.2"}, {"name": "matplotlib", "version": "3.9.3"}, {"name": "tensorboard", "version": "2.18.0"}, {"name": "tensorboardX", "version": "2.6.2.2"} diff --git a/tests/modules/applications/qml/generative_modeling/transformation/test_MinMax.py b/tests/modules/applications/qml/generative_modeling/transformation/test_min_max.py similarity index 67% rename from tests/modules/applications/qml/generative_modeling/transformation/test_MinMax.py rename to tests/modules/applications/qml/generative_modeling/transformation/test_min_max.py index 1260c444b..9f7d9ffd0 100644 --- a/tests/modules/applications/qml/generative_modeling/transformation/test_MinMax.py +++ b/tests/modules/applications/qml/generative_modeling/transformation/test_min_max.py @@ -3,10 +3,10 @@ import numpy as np -from modules.applications.qml.generative_modeling.transformations.Transformation import Transformation -from modules.applications.qml.generative_modeling.transformations.MinMax import MinMax -from modules.applications.qml.generative_modeling.circuits.CircuitStandard import CircuitStandard -from modules.applications.qml.generative_modeling.circuits.CircuitCardinality import CircuitCardinality +from modules.applications.qml.generative_modeling.transformations.transformation import Transformation +from modules.applications.qml.generative_modeling.transformations.min_max import MinMax +from modules.applications.qml.generative_modeling.circuits.circuit_standard import CircuitStandard +from modules.applications.qml.generative_modeling.circuits.circuit_cardinality import CircuitCardinality class TestMinMax(unittest.TestCase): @@ -73,31 +73,20 @@ def test_transform(self): self.assertEqual(transformed_config["train_size"], 0.8, "Expected train size to match.") def test_reverse_transform(self): - input_data = { - "best_sample": np.array([2, 1, 0, 3]), # Example results aligned with bins - "depth": 3, - "architecture_name": "TestArchitecture", - "n_qubits": 4, - "KL": [0.1, 0.2, 0.05], - "best_parameter": [0.5, 1.0], - "circuit_transpiled": None, - "store_dir_iter": "/tmp" - } - - # Simulate the transformation configuration - self.minmax_instance.transform_config = { - "n_registers": 4 + # self.minmax_instance.transform(self.sample_input_data, self.sample_config) + best_sample = np.random.randint(0, 64, size=(64,)) + # Mock the input for reverse_transform + reverse_input_data = { + "best_sample": best_sample, + "depth": 1, + "architecture_name": "test_arch", + "n_qubits": 6, + "KL": [0.1], + "best_parameter": [0.5], + "store_dir_iter": "test_dir", + "circuit_transpiled": None } - self.minmax_instance.histogram_train = np.array([0.1, 0.2]) - self.minmax_instance.histogram_train_original = np.array([0.05, 0.15]) - - # Mock Transformation methods for alignment - Transformation.compute_discretization_efficient = MagicMock(return_value=np.array([[0], [1], [2], [3]])) - Transformation.generate_samples_efficient = MagicMock(return_value=np.array([[0], [1], [2], [3]])) - # Call reverse_transform - reversed_config = self.minmax_instance.reverse_transform(input_data) + reverse_config = self.minmax_instance.reverse_transform(reverse_input_data) - # Assertions - self.assertIn("generated_samples", reversed_config, "Expected 'generated_samples' in the output.") - self.assertIn("histogram_generated", reversed_config, "Expected 'histogram_generated' in the output.") + self.assertIn("generated_samples", reverse_config) diff --git a/tests/modules/applications/qml/generative_modeling/transformation/test_PIT.py b/tests/modules/applications/qml/generative_modeling/transformation/test_pit.py similarity index 51% rename from tests/modules/applications/qml/generative_modeling/transformation/test_PIT.py rename to tests/modules/applications/qml/generative_modeling/transformation/test_pit.py index 12327ba8d..bbfbb71d0 100644 --- a/tests/modules/applications/qml/generative_modeling/transformation/test_PIT.py +++ b/tests/modules/applications/qml/generative_modeling/transformation/test_pit.py @@ -1,11 +1,9 @@ import unittest - import numpy as np +from unittest.mock import MagicMock -from modules.applications.qml.generative_modeling.circuits.CircuitCopula import \ - CircuitCopula -from modules.applications.qml.generative_modeling.transformations.PIT import \ - PIT +from modules.applications.qml.generative_modeling.transformations.pit import PIT +from modules.applications.qml.generative_modeling.circuits.circuit_copula import CircuitCopula class TestPIT(unittest.TestCase): @@ -24,9 +22,17 @@ def setUpClass(cls): # Mock reverse_epit_lookup for testing cls.pit_instance.reverse_epit_lookup = np.array([ [0.1, 0.2, 0.3, 0.4], - [0.5, 0.6, 0.7, 0.8] + [0.5, 0.6, 0.7, 0.8], + [0.9, 1.0, 1.1, 1.2] ]) - # cls.pit_instance.grid_shape = (2, 2) + cls.pit_instance.grid_shape = (2, 2) + cls.pit_instance.transform_config = { + "n_registers": 2, + "binary_train": np.array([[0, 1], [1, 0]]), + "histogram_train": np.array([0.5, 0.5]), + "dataset_name": "mock_dataset", + "store_dir_iter": "/mock/path" + } def test_get_requirements(self): requirements = self.pit_instance.get_requirements() @@ -52,27 +58,38 @@ def test_transform(self): self.assertIsInstance(result["binary_train"], np.ndarray, "Expected binary_train to be a numpy array.") self.assertEqual(result["n_qubits"], self.sample_input_data["n_qubits"], "n_qubits mismatch.") - def test_reverse_transform(self): - """ - Test the reverse_transform method. - """ - input_data = { - "depth": 1, - "architecture_name": "dummy_arch", - "n_qubits": 4, - "KL": [0.1, 0.05], - "best_sample": np.random.rand(10, 2), - "circuit_transpiled": "dummy_circuit", - "best_parameter": [0.5, 0.3], - "store_dir_iter": "/tmp", - } - self.pit_instance.grid_shape = 10 - self.pit_instance.transform_config = { - "n_registers": 2 - } - result = self.pit_instance.reverse_transform(input_data) - self.assertIn("generated_samples", result) - self.assertIn("KL_best_transformed", result) + # This test is currently commented out because: + # - The `reverse_transform` method relies on mocked internal methods (`compute_discretization_efficient` and + # `generate_samples_efficient`) that require precise mocking of their behavior and returned data. + # - Creating realistic mock data for `reverse_transform` is challenging without deeper understanding of + # the expected transformations or how they interact with the architecture. + # - We plan to implement this test in the future when there is more clarity on the expected functionality + # def test_reverse_transform(self): + # # Mocked input data + # input_data = { + # "best_sample": np.array([0, 1, 2, 3]), + # "depth": 2, + # "architecture_name": "TestArchitecture", + # "n_qubits": 2, + # "KL": [0.1, 0.2], + # "circuit_transpiled": None, + # "best_parameter": [0.5, 0.6], + # "store_dir_iter": "/mock/path" + # } + + # # Mock internal method responses + # self.pit_instance.compute_discretization_efficient = MagicMock(return_value=np.array([[0, 1], [2, 3]])) + # self.pit_instance.generate_samples_efficient = MagicMock(return_value=np.array([[0.1, 0.2], [0.3, 0.4]])) + + # # Call the method + # reverse_config = self.pit_instance.reverse_transform(input_data) + + # # Validate the response + # self.assertIn("generated_samples", reverse_config) + # self.assertIn("transformed_samples", reverse_config) + # self.assertIn("KL_best_transformed", reverse_config) + # self.assertEqual(reverse_config["depth"], input_data["depth"]) + # self.assertEqual(reverse_config["dataset_name"], self.pit_instance.dataset_name) def test_emp_integral_trans(self): data = np.random.uniform(0, 1, 100) @@ -90,3 +107,14 @@ def test_inverse_transform(self): self.pit_instance.fit_transform(data) inverse_data = self.pit_instance.inverse_transform(data) self.assertEqual(inverse_data.shape, data.shape, "Inverse-transformed data should match the input shape.") + + # This test is currently commented out because: + # We plan to revisit this test in the future + # def test_reverse_empirical_integral_trans_single(self): + # self.pit_instance.reverse_epit_lookup = np.array([ + # [0.1, 0.2, 0.3], + # [0.4, 0.5, 0.6] + # ]) + # values = np.array([0.2, 0.8]) + # reverse_result = self.pit_instance._reverse_emp_integral_trans_single(values) + # self.assertEqual(len(reverse_result), 1, "Reverse transformed result length mismatch.") diff --git a/tests/test_BenchmarkManager.py b/tests/test_benchmark_manager.py similarity index 90% rename from tests/test_BenchmarkManager.py rename to tests/test_benchmark_manager.py index 96d7e4ddb..d75f0dd7d 100644 --- a/tests/test_BenchmarkManager.py +++ b/tests/test_benchmark_manager.py @@ -4,7 +4,7 @@ from pathlib import Path from unittest.mock import MagicMock, mock_open, patch -from src.BenchmarkManager import BenchmarkManager, Instruction +from src.benchmark_manager import BenchmarkManager, Instruction class TestBenchmarkManager(unittest.TestCase): @@ -43,8 +43,8 @@ def test_load_interrupted_results_no_file(self, mock_path_exists): results = self.benchmark_manager.load_interrupted_results() self.assertIsNone(results) - @patch("BenchmarkManager.Path.mkdir") # Mock Path.mkdir - @patch("BenchmarkManager.logging.FileHandler") # Mock FileHandler + @patch("benchmark_manager.Path.mkdir") # Mock Path.mkdir + @patch("benchmark_manager.logging.FileHandler") # Mock FileHandler def test_create_store_dir(self, mock_file_handler, mock_path_mkdir): # Mock datetime to control the generated timestamp dynamic_now = datetime.today() @@ -79,9 +79,9 @@ def test_set_logger(self, mock_get_logger, mock_file_handler): mock_file_handler.assert_called_with("/mock/store/logging.log") logger_mock.addHandler.assert_called_once() - @patch("BenchmarkManager.Path.mkdir") + @patch("benchmark_manager.Path.mkdir") @patch("os.path.exists", return_value=True) - @patch("BenchmarkManager.logging.FileHandler") + @patch("benchmark_manager.logging.FileHandler") def test_resume_store_dir(self, mock_file_handler, mock_path_exists, mock_path_mkdir): store_dir = "/mock_dir" self.benchmark_manager._resume_store_dir(store_dir) @@ -104,9 +104,9 @@ def test_save_as_json(self, mock_json_dump, mock_open_file): mock_open_file.assert_called_once_with(f"{self.benchmark_manager.store_dir}/results.json", 'w') mock_json_dump.assert_called_once_with(mock_results, mock_open_file(), indent=2) - @patch("src.BenchmarkManager.Plotter.visualize_results") - @patch("src.BenchmarkManager.BenchmarkManager._save_as_json") - @patch("src.BenchmarkManager.ConfigManager") + @patch("src.benchmark_manager.Plotter.visualize_results") + @patch("src.benchmark_manager.BenchmarkManager._save_as_json") + @patch("src.benchmark_manager.ConfigManager") def test_summarize_results(self, mock_config_manager, mock_save_json, mock_visualize): self.benchmark_manager.summarize_results(["/mock/dir1", "/mock/dir2"]) mock_save_json.assert_called() @@ -119,8 +119,8 @@ def test_load_results(self, mock_open_file, mock_glob): self.assertEqual(len(results), 4, "Expected to load results from both directories.") self.assertEqual(results[0]["result"], "mock", "Expected the first result to match mock.") - @patch("src.BenchmarkManager.preprocess") - @patch("src.BenchmarkManager.postprocess") + @patch("src.benchmark_manager.preprocess") + @patch("src.benchmark_manager.postprocess") def test_traverse_config(self, mock_postprocess, mock_preprocess): # Mock the preprocess function to return expected values mock_preprocess.return_value = (Instruction.PROCEED, "processed_input", 0.1) @@ -149,8 +149,8 @@ def test_traverse_config(self, mock_postprocess, mock_preprocess): self.assertEqual(output, "postprocessed_output", "Expected processed output to match mock postprocess return.") self.assertIsNotNone(benchmark_record, "Expected a BenchmarkRecord instance.") - @patch("BenchmarkManager.BenchmarkManager._collect_all_results", return_value=[{"key": "value"}]) - @patch("BenchmarkManager.BenchmarkManager._save_as_json") + @patch("benchmark_manager.BenchmarkManager._collect_all_results", return_value=[{"key": "value"}]) + @patch("benchmark_manager.BenchmarkManager._save_as_json") def test_orchestrate_benchmark(self, mock_save_as_json, mock_collect_all_results): mock_config_manager = MagicMock() mock_config_manager.get_config.return_value = {"application": {"name": "test_app"}} diff --git a/tests/test_ConfigManager.py b/tests/test_config_manager.py similarity index 96% rename from tests/test_ConfigManager.py rename to tests/test_config_manager.py index 4313c498a..e96591aa1 100644 --- a/tests/test_ConfigManager.py +++ b/tests/test_config_manager.py @@ -1,8 +1,8 @@ import unittest from unittest.mock import MagicMock, patch -from modules.Core import Core -from src.ConfigManager import ConfigManager +from modules.core import Core +from src.config_manager import ConfigManager class TestConfigManager(unittest.TestCase): @@ -10,8 +10,8 @@ class TestConfigManager(unittest.TestCase): def setUp(self): self.config_manager = ConfigManager() - @patch("src.ConfigManager.inquirer.prompt") - @patch("src.ConfigManager.checkbox") + @patch("src.config_manager.inquirer.prompt") + @patch("src.config_manager.checkbox") def test_query_module(self, mock_checkbox, mock_prompt): # Mock responses for checkbox and prompt mock_checkbox.return_value = {"param1": [1, 2]} # Simulates a user selecting 1 and 2 @@ -96,7 +96,7 @@ def test_translate_legacy_config_helper(self): self.assertEqual(result[0]["name"], "SolverA") self.assertEqual(len(result[0]["submodules"]), 1) - @patch("src.ConfigManager._get_instance_with_sub_options") + @patch("src.config_manager._get_instance_with_sub_options") def test_load_config(self, mock_get_instance): mock_app_instance = MagicMock() mock_get_instance.return_value = mock_app_instance @@ -218,8 +218,8 @@ def test_create_tree_figure(self): self.config_manager.create_tree_figure("/mock/store/dir") mock_savefig.assert_called_once_with("/mock/store/dir/BenchmarkGraph.png", format="PNG") - @patch("src.ConfigManager.inquirer.prompt") - @patch("src.ConfigManager.checkbox") + @patch("src.config_manager.inquirer.prompt") + @patch("src.config_manager.checkbox") def test_query_for_config(self, mock_checkbox, mock_prompt): mock_checkbox.return_value = {"param1": [1, 2, "Custom Input"]} mock_prompt.side_effect = [{"custom_input": "custom_value"}] diff --git a/tests/test_Main.py b/tests/test_main.py similarity index 100% rename from tests/test_Main.py rename to tests/test_main.py