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 (>= 1.1.0, < 2) (doi:10.5281/zenodo.17116431)
- TensorFlow (== 2.*)
- NumPy (>= 1.21, < 3)
Additional requirements for testing
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 1.1.0. Please see the Change Log for more details. QuGrad uses semantic versioning.
1 maintainer
Metadata
1 maintainer
Release files for qugrad 1.1.0
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| qugrad-1.1.0.tar.gz | 39.7 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| qugrad-1.1.0-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 63.5 kB
Release files / qugrad-1.1.0.tar.gz
| Download URL | qugrad-1.1.0.tar.gz |
|---|---|
| Size | 39.7 kB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
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Transparency logRelease files / qugrad-1.1.0-py3-none-any.whl
| Download URL | qugrad-1.1.0-py3-none-any.whl |
|---|---|
| Size | 23.8 kB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
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BLAKE2b-256 checksum How to use checksums |
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| Upload date | |
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Uploaded using Trusted Publishing? What is trusted publishing? |
Yes |
| Uploaded via |
twine/6.1.0 CPython/3.13.12
|
Provenance
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PyPI verified that this artifact, at this checksum, originated from the publisher listed below.
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