Python package for writing, manipulating, and running quantum circuits on quantum computers and simulators.
Features – Installation – Quick Start – Documentation – Integrations – Community – Citing Cirq – Contact
Cirq provides useful abstractions for dealing with today’s noisy intermediate-scale quantum (NISQ) computers, where the details of quantum hardware are vital to achieving state-of-the-art results. Some of its features include:
- Flexible gate definitions and custom gates
- Parameterized circuits with symbolic variables
- Circuit transformation, compilation and optimization
- Hardware device modeling
- Noise modeling
- Multiple built-in quantum circuit simulators
- Integration with qsim for high-performance simulation
- Interoperability with NumPy and SciPy
- Cross-platform compatibility
Cirq supports Python version 3.10 and later, and can be used on Linux, MacOS, and Windows, as well as Google Colab. For complete installation instructions, please refer to the Install section of the online Cirq documentation.
Here is a simple example to get you up and running with Cirq after you have installed it. Start a Python interpreter, and then type the following:
import cirq
# Pick a qubit.
qubit = cirq.GridQubit(0, 0)
# Create a circuit.
circuit = cirq.Circuit(
cirq.X(qubit)**0.5, # Square root of NOT.
cirq.measure(qubit, key='m') # Measurement.
)
print("Circuit:")
print(circuit)
# Simulate the circuit several times.
simulator = cirq.Simulator()
result = simulator.run(circuit, repetitions=20)
print("Results:")
print(result)Python should then print output similar to this:
Circuit:
(0, 0): ───X^0.5───M('m')───
Results:
m=11000111111011001000
Congratulations! You have run your first quantum simulation in Cirq. You can continue to learn more by exploring the many Cirq tutorials described below.
The primary documentation site for Cirq is the Cirq home page on the Quantum AI website. There and elsewhere, a variety of documentation for Cirq is available.
- Video tutorials on YouTube are an engaging way to learn Cirq.
- Jupyter notebook-based tutorials let you learn Cirq from your browser – no installation needed.
- Text-based tutorials on the Cirq home page are great when combined with a local installation of Cirq on your computer. After starting with the basics, you'll be ready to dive into tutorials on circuit building and circuit simulation under the Build and Simulate tabs, respectively. Check out the other tabs for more!
- Docs for the current stable release correspond to what you get with
pip install cirq. - Docs for the pre-release correspond to what you get with
pip install cirq~=1.0.dev.
- The examples subdirectory of the Cirq GitHub repo has many programs illustrating the application of Cirq to everything from common textbook algorithms to more advanced methods.
- The Experiments page on the Cirq documentation site has yet more examples, from simple to advanced.
- The Cirq releases page on GitHub lists the changes in each release.
Google Quantum AI has a suite of open-source software that lets you do more with Cirq. From high-performance simulators, to novel tools for expressing and analyzing fault-tolerant quantum algorithms, our software stack lets you develop quantum programs for a variety of applications.
| Your interests | Software to explore |
|---|---|
| Quantum algorithms? Fault-tolerant quantum computing (FTQC)? |
Qualtran |
| Large circuits and/or a lot of simulations? | qsim |
| Circuits with thousands of qubits and millions of Clifford operations? | Stim |
| Quantum error correction (QEC)? | Stim |
| Chemistry and/or material science? | OpenFermion OpenFermion-FQE OpenFermion-PySCF OpenFermion-Psi4 |
| Quantum machine learning (QML)? | TensorFlow Quantum |
| Real experiments using Cirq? | ReCirq |
Cirq has benefited from open-source contributions by over 200 people and counting. We are dedicated to cultivating an open and inclusive community to build software for quantum computers, and have a code of conduct for our community.
Stay on top of Cirq developments using the approach that best suits your needs:
- For releases and major announcements: sign up to the low-volume mailing list
cirq-announce. - For releases only:
- Via GitHub notifications: configure repository notifications for Cirq.
- Via Atom/RSS from GitHub: subscribe to the GitHub Cirq releases Atom feed.
- Via RSS from PyPI: subscribe to the PyPI releases RSS feed for Cirq.
Cirq releases take place approximately every quarter.
- Do you have questions about using Cirq? Post them to the Quantum Computing
Stack Exchange and tag them with the
cirqtag. You can also search past questions using that tag – it's a great way to learn! - Would you like to get more involved in Cirq development? Cirq Cynq is our biweekly virtual meeting of contributors to discuss everything from issues to ongoing efforts, as well as to ask questions. Become a member of cirq-dev to get an automatic meeting invitation!
- Do you have a feature request or want to report a bug? Open an issue on GitHub to report it!
- Do you have a code contribution? Read our contribution guidelines, then open a pull request!
When publishing articles or otherwise writing about Cirq, please cite the Cirq version you use – it will help others reproduce your results. We use Zenodo to preserve releases. The following links let you download the bibliographic record for the latest stable release of Cirq in some popular formats:
For formatted citations and records in other formats, as well as records for all releases of Cirq past and present, please visit the Cirq page on Zenodo.
For any questions or concerns not addressed here, please email [email protected].
Cirq is not an official Google product. Copyright 2019 The Cirq Developers.