QuGrad¶

A Python package for quantum optimal control.

Installation¶

The python package can be installed with pip as follows:

pip install qugrad

If on Linux and using a conda environment you may encounter an error

version `GLIBCXX_...' not found

to fix this you also need to execute:

conda install -c conda-forge libstdcxx-ng

Requirements¶

Requires:

PySTE (== 1.*) (doi:10.5281/zenodo.17116431)
TensorFlow (== 2.*)
NumPy (>= 1.21, < 3)

Additional requirements for testing¶

toml
PyYAML

Documentation¶

Documentation, including worked examples can be found at: https://QuGrad.readthedocs.io

Source Code¶

Source code can be found at: https://github.com/Christopher-K-Long/QuGrad

A mirror can be found at: https://gitlab.com/Christopher-K-Long/QuGrad

Please submit all pull requests, issues, discussions, and vulnerability reports to the GitHub repository.

Releases from this repository are assigned DOIs and can be found at https://doi.org/10.5281/zenodo.17116721. The DOI for all releases is 10.5281/zenodo.17116721. Additionally, the releases are archived to https://archive.softwareheritage.org/swh:1:dir:842d13c2aaa42faf212ecd804f6ed151a65436cb.

Version and Changes¶

The current version is 1.0.2. Please see the Change Log for more details. QuGrad uses semantic versioning.