OptimalTraining_RFQRC

Implementation of recurrence-free quantum reservoir computing (RF-QRC) with denoising methods for optimal training under finite sampling.

Repository Structure

• src/Notebook.ipynb – Main workflow notebook: training and prediction with RF-QRC, QRC, and classical reservoirs.
• src/QRC/rfqrc.py – Recurrence-free quantum reservoir implementation (Qiskit).
• src/QRC/qrc.py – Standard quantum reservoir implementation.
• src/QRC/crc.py – Classical reservoir computing / Echo State Network implementation.
• src/QRC/denoise.py – Denoising routines (SVD truncation, filtering).
• src/QRC/systems.py – Dynamical systems definitions (e.g., Lorenz63, Lorenz96).
• src/QRC/validation.py – Hyperparameter search and recycle validation routines.

Usage

Install dependencies:

pip install -r requirements.txt

Then run the following notebook

python Notebook.ipynb

Citation

If you use this code in your research, please cite the corresponding paper:

Robust quantum reservoir computers for forecasting chaotic dynamics: generalized synchronization and stability (https://arxiv.org/abs/2506.22335)