QuantRL: Quantum Control using Reinforcement Learning

A library of modules to interface deterministic and stochastic quantum models for reinforcement learning.

Key Features!

• Quickly interface environments for Reinforcement Learning using Stable-Baselines3.
• Run multiple environments in parallel using vectorized inheritable classes.
• Support for deterministic and stochastic linear environments.
• Live visualization and learning curves.

What's New in v0.0.7

• Added support for measurement noise in observations.
• Updated stochastic environment for fast Wiener processes.
• Asynchronous cache-dump to speed up environment evolution.
• Callback to save best mean reward.

What's New in v0.0.6

• Initialize environments with any of the three backends: NumPy, PyTorch and JAX.
• Solve IVPs for the popular libraries TorchDiffEq and Diffrax.

Installation

QuantRL requires Python 3.10+, preferably installed via the Anaconda distribution. The toolbox primarily relies on gymnasium (for single environments) and stable-baselines3 (for vectorized environments). All of its dependencies can be installed using:

conda install "numpy<2.0.0" scipy matplotlib tqdm pillow pandas gymnasium stable-baselines3

Additionally, to avail the PyTorch or JAX backends, the latest version of these framework (for both CPU and GPU) should be installed (preferably in different conda environments) using in their official documentations: PyTorch docs and JAX docs. After successful installation, the corresponding libraries (torchdiffeq for PyTorch and diffrax for JAX) can be installed using PIP as:

pip install torchdiffeq

or,

pip install jax
pip install diffrax

To install JAX with GPU support, use jax[cuda12].

Note: JAX-GPU support for Windows and MacOS is still limited but it runs well in WSL2.

Finally, to install the latest version of quantrl, execute:

pip install git+https://github.com/sampreet/quantrl.git