feedback-grape 0.0.1rc2

A package for GRAPE with feedback

feedbackGRAPE

This is the main repository for the feedbackGRAPE package (under development), eventually offering:

• vectorized, GPU-enabled, differentiable simulations of driven dissipative quantum systems via jax
• efficient quantum optimal control (gradient-ascent pulse engineering, GRAPE)
• including feedback (using the newly developed feedbackGRAPE approach)

Think of parallelized, highly efficient qutip with feedback control.

Installation

To install dependencies necessary for the package
pip install -U -r requirements.txt
To install dependencies necessary for testing, linting, formating, ...
pip install -U -r requirements_dev.txt
To be able to render the jupyter notebooks, make sure to install pandoc, in conda the command is:
conda install conda-forge::pandoc
To be able to run your code on GPUs please make sure to install jax[cuda12] using the following command:
pip install -U "jax[cuda12]==0.5.2" or
pip install -U -r requirements_gpu.txt

Documentation

For Development: Enter the command cd docs then make html and then open index.html in docs/build/html with Live Server to take a look at the Documentaion.

Testing

Simply type pytest. This would also generate a coverage report.

checking for dynamically typed errors

Simply type mypy feedback_grape. This would give you type checking errors if any.

linting and formating

For Linting ruff check
For Formating ruff format

Before Commiting and Pushing

Simply type tox. This would test the code on different environments.

References

FeedbackGRAPE was introduced in Porotti, Peano, Marquardt, PRX Quantum 4, 030305 (2023). It enables the addition of feedback to GRAPE-style numerical quantum optimal control, important for modern tasks like quantum state stabilization or quantum error correction. In addition, it is formulated in such a way that the use of neural networks and modern autodifferentiation frameworks is easy. See a first full-fledged application to the Gottesman-Kitaev-Preskill bosonic code quantum error correction in Puviani et al, Phys. Rev. Lett. 134, 020601 (2025). Both of these papers come with github repositories, but with code specialized to the examples and use cases: rporotti/feedback_grape and Matteo-Puviani/GQF. The purpose of the framework offered in this repository here is to make it very easy for everyone to do the same for their own problems, with minimum overhead.