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diff --git a/python-pennylane.spec b/python-pennylane.spec new file mode 100644 index 0000000..00bf7eb --- /dev/null +++ b/python-pennylane.spec @@ -0,0 +1,560 @@ +%global _empty_manifest_terminate_build 0 +Name: python-PennyLane +Version: 0.29.1 +Release: 1 +Summary: PennyLane is a Python quantum machine learning library by Xanadu Inc. +License: Apache License 2.0 +URL: https://github.com/XanaduAI/pennylane +Source0: https://mirrors.nju.edu.cn/pypi/web/packages/0e/e8/6d3c7fa27743c198073db9a62ed57ed9b3a767cda49b220c6b08405f6f27/PennyLane-0.29.1.tar.gz +BuildArch: noarch + +Requires: python3-numpy +Requires: python3-scipy +Requires: python3-networkx +Requires: python3-retworkx +Requires: python3-autograd +Requires: python3-toml +Requires: python3-appdirs +Requires: python3-semantic-version +Requires: python3-autoray +Requires: python3-cachetools +Requires: python3-pennylane-lightning +Requires: python3-requests +Requires: python3-cvxpy +Requires: python3-cvxopt + +%description +<p align="center"> + <!-- Tests (GitHub actions) --> + <a href="https://github.com/PennyLaneAI/pennylane/actions?query=workflow%3ATests"> + <img src="https://img.shields.io/github/actions/workflow/status/PennyLaneAI/PennyLane/tests.yml?branch=master&style=flat-square" /> + </a> + <!-- CodeCov --> + <a href="https://codecov.io/gh/PennyLaneAI/pennylane"> + <img src="https://img.shields.io/codecov/c/github/PennyLaneAI/pennylane/master.svg?logo=codecov&style=flat-square" /> + </a> + <!-- ReadTheDocs --> + <a href="https://docs.pennylane.ai/en/latest"> + <img src="https://readthedocs.com/projects/xanaduai-pennylane/badge/?version=latest&style=flat-square" /> + </a> + <!-- PyPI --> + <a href="https://pypi.org/project/PennyLane"> + <img src="https://img.shields.io/pypi/v/PennyLane.svg?style=flat-square" /> + </a> + <!-- Forum --> + <a href="https://discuss.pennylane.ai"> + <img src="https://img.shields.io/discourse/https/discuss.pennylane.ai/posts.svg?logo=discourse&style=flat-square" /> + </a> + <!-- License --> + <a href="https://www.apache.org/licenses/LICENSE-2.0"> + <img src="https://img.shields.io/pypi/l/PennyLane.svg?logo=apache&style=flat-square" /> + </a> +</p> + +<p align="center"> + <a href="https://pennylane.ai">PennyLane</a> is a cross-platform Python library for <a + href="https://en.wikipedia.org/wiki/Differentiable_programming">differentiable + programming</a> of quantum computers. +</p> + +<p align="center"> + <strong>Train a quantum computer the same way as a neural network.</strong> + <img src="https://raw.githubusercontent.com/PennyLaneAI/pennylane/master/doc/_static/header.png#gh-light-mode-only" width="700px"> + <!-- + Use a relative import for the dark mode image. When loading on PyPI, this + will fail automatically and show nothing. + --> + <img src="./doc/_static/header-dark-mode.png#gh-dark-mode-only" width="700px" onerror="this.style.display='none'" alt=""/> +</p> + +## Key Features + +<img src="https://raw.githubusercontent.com/PennyLaneAI/pennylane/master/doc/_static/code.png" width="400px" align="right"> + +- *Machine learning on quantum hardware*. Connect to quantum hardware using **PyTorch**, **TensorFlow**, **JAX**, **Keras**, or **NumPy**. Build rich and flexible hybrid quantum-classical models. + +- *Device-independent*. Run the same quantum circuit on different quantum backends. Install + [plugins](https://pennylane.ai/plugins.html) to access even more devices, including **Strawberry + Fields**, **Amazon Braket**, **IBM Q**, **Google Cirq**, **Rigetti Forest**, **Qulacs**, **Pasqal**, **Honeywell**, and more. + +- *Follow the gradient*. Hardware-friendly **automatic differentiation** of quantum circuits. + +- *Batteries included*. Built-in tools for **quantum machine learning**, **optimization**, and + **quantum chemistry**. Rapidly prototype using built-in quantum simulators with + backpropagation support. + +## Installation + +PennyLane requires Python version 3.8 and above. Installation of PennyLane, as well as all +dependencies, can be done using pip: + +```console +python -m pip install pennylane +``` + +## Docker support + +**Docker** support exists for building using **CPU** and **GPU** (Nvidia CUDA +11.1+) images. [See a more detailed description +here](https://pennylane.readthedocs.io/en/stable/development/guide/installation.html#docker). + +## Getting started + +For an introduction to quantum machine learning, guides and resources are available on +PennyLane's [quantum machine learning hub](https://pennylane.ai/qml/): + +<img src="https://raw.githubusercontent.com/PennyLaneAI/pennylane/master/doc/_static/readme/gpu_to_qpu.png" align="right" width="400px"> + +* [What is quantum machine learning?](https://pennylane.ai/qml/whatisqml.html) +* [QML tutorials and demos](https://pennylane.ai/qml/demonstrations.html) +* [Frequently asked questions](https://pennylane.ai/faq.html) +* [Key concepts of QML](https://pennylane.ai/qml/glossary.html) +* [QML videos](https://pennylane.ai/qml/videos.html) + +You can also check out our [documentation](https://pennylane.readthedocs.io) for [quickstart +guides](https://pennylane.readthedocs.io/en/stable/introduction/pennylane.html) to using PennyLane, +and detailed developer guides on [how to write your +own](https://pennylane.readthedocs.io/en/stable/development/plugins.html) PennyLane-compatible +quantum device. + +## Tutorials and demonstrations + +Take a deeper dive into quantum machine learning by exploring cutting-edge algorithms on our [demonstrations +page](https://pennylane.ai/qml/demonstrations.html). + +<a href="https://pennylane.ai/qml/demonstrations.html"> + <img src="https://raw.githubusercontent.com/PennyLaneAI/pennylane/master/doc/_static/readme/demos.png" width="900px"> +</a> + +All demonstrations are fully executable, and can be downloaded as Jupyter notebooks and Python +scripts. + +If you would like to contribute your own demo, see our [demo submission +guide](https://pennylane.ai/qml/demos_submission.html). + +## Videos + +Seeing is believing! Check out [our videos](https://pennylane.ai/qml/videos.html) to learn about +PennyLane, quantum computing concepts, and more. + +<a href="https://pennylane.ai/qml/videos.html"> + <img src="https://raw.githubusercontent.com/PennyLaneAI/pennylane/master/doc/_static/readme/videos.png" width="900px"> +</a> + +## Contributing to PennyLane + +We welcome contributions—simply fork the PennyLane repository, and then make a [pull +request](https://help.github.com/articles/about-pull-requests/) containing your contribution. All +contributors to PennyLane will be listed as authors on the releases. All users who contribute +significantly to the code (new plugins, new functionality, etc.) will be listed on the PennyLane +arXiv paper. + +We also encourage bug reports, suggestions for new features and enhancements, and even links to cool +projects or applications built on PennyLane. + +See our [contributions +page](https://github.com/PennyLaneAI/pennylane/blob/master/.github/CONTRIBUTING.md) and our +[developer hub](https://pennylane.readthedocs.io/en/stable/development/guide.html) for more +details. + +## Support + +- **Source Code:** https://github.com/PennyLaneAI/pennylane +- **Issue Tracker:** https://github.com/PennyLaneAI/pennylane/issues + +If you are having issues, please let us know by posting the issue on our GitHub issue tracker. + +We also have a [PennyLane discussion forum](https://discuss.pennylane.ai)—come join the community +and chat with the PennyLane team. + +Note that we are committed to providing a friendly, safe, and welcoming environment for all. +Please read and respect the [Code of Conduct](.github/CODE_OF_CONDUCT.md). + +## Authors + +PennyLane is the work of [many contributors](https://github.com/PennyLaneAI/pennylane/graphs/contributors). + +If you are doing research using PennyLane, please cite [our paper](https://arxiv.org/abs/1811.04968): + +> Ville Bergholm et al. *PennyLane: Automatic differentiation of hybrid quantum-classical +> computations.* 2018. arXiv:1811.04968 + +## License + +PennyLane is **free** and **open source**, released under the Apache License, Version 2.0. + + +%package -n python3-PennyLane +Summary: PennyLane is a Python quantum machine learning library by Xanadu Inc. +Provides: python-PennyLane +BuildRequires: python3-devel +BuildRequires: python3-setuptools +BuildRequires: python3-pip +%description -n python3-PennyLane +<p align="center"> + <!-- Tests (GitHub actions) --> + <a href="https://github.com/PennyLaneAI/pennylane/actions?query=workflow%3ATests"> + <img src="https://img.shields.io/github/actions/workflow/status/PennyLaneAI/PennyLane/tests.yml?branch=master&style=flat-square" /> + </a> + <!-- CodeCov --> + <a href="https://codecov.io/gh/PennyLaneAI/pennylane"> + <img src="https://img.shields.io/codecov/c/github/PennyLaneAI/pennylane/master.svg?logo=codecov&style=flat-square" /> + </a> + <!-- ReadTheDocs --> + <a href="https://docs.pennylane.ai/en/latest"> + <img src="https://readthedocs.com/projects/xanaduai-pennylane/badge/?version=latest&style=flat-square" /> + </a> + <!-- PyPI --> + <a href="https://pypi.org/project/PennyLane"> + <img src="https://img.shields.io/pypi/v/PennyLane.svg?style=flat-square" /> + </a> + <!-- Forum --> + <a href="https://discuss.pennylane.ai"> + <img src="https://img.shields.io/discourse/https/discuss.pennylane.ai/posts.svg?logo=discourse&style=flat-square" /> + </a> + <!-- License --> + <a href="https://www.apache.org/licenses/LICENSE-2.0"> + <img src="https://img.shields.io/pypi/l/PennyLane.svg?logo=apache&style=flat-square" /> + </a> +</p> + +<p align="center"> + <a href="https://pennylane.ai">PennyLane</a> is a cross-platform Python library for <a + href="https://en.wikipedia.org/wiki/Differentiable_programming">differentiable + programming</a> of quantum computers. +</p> + +<p align="center"> + <strong>Train a quantum computer the same way as a neural network.</strong> + <img src="https://raw.githubusercontent.com/PennyLaneAI/pennylane/master/doc/_static/header.png#gh-light-mode-only" width="700px"> + <!-- + Use a relative import for the dark mode image. When loading on PyPI, this + will fail automatically and show nothing. + --> + <img src="./doc/_static/header-dark-mode.png#gh-dark-mode-only" width="700px" onerror="this.style.display='none'" alt=""/> +</p> + +## Key Features + +<img src="https://raw.githubusercontent.com/PennyLaneAI/pennylane/master/doc/_static/code.png" width="400px" align="right"> + +- *Machine learning on quantum hardware*. Connect to quantum hardware using **PyTorch**, **TensorFlow**, **JAX**, **Keras**, or **NumPy**. Build rich and flexible hybrid quantum-classical models. + +- *Device-independent*. Run the same quantum circuit on different quantum backends. Install + [plugins](https://pennylane.ai/plugins.html) to access even more devices, including **Strawberry + Fields**, **Amazon Braket**, **IBM Q**, **Google Cirq**, **Rigetti Forest**, **Qulacs**, **Pasqal**, **Honeywell**, and more. + +- *Follow the gradient*. Hardware-friendly **automatic differentiation** of quantum circuits. + +- *Batteries included*. Built-in tools for **quantum machine learning**, **optimization**, and + **quantum chemistry**. Rapidly prototype using built-in quantum simulators with + backpropagation support. + +## Installation + +PennyLane requires Python version 3.8 and above. Installation of PennyLane, as well as all +dependencies, can be done using pip: + +```console +python -m pip install pennylane +``` + +## Docker support + +**Docker** support exists for building using **CPU** and **GPU** (Nvidia CUDA +11.1+) images. [See a more detailed description +here](https://pennylane.readthedocs.io/en/stable/development/guide/installation.html#docker). + +## Getting started + +For an introduction to quantum machine learning, guides and resources are available on +PennyLane's [quantum machine learning hub](https://pennylane.ai/qml/): + +<img src="https://raw.githubusercontent.com/PennyLaneAI/pennylane/master/doc/_static/readme/gpu_to_qpu.png" align="right" width="400px"> + +* [What is quantum machine learning?](https://pennylane.ai/qml/whatisqml.html) +* [QML tutorials and demos](https://pennylane.ai/qml/demonstrations.html) +* [Frequently asked questions](https://pennylane.ai/faq.html) +* [Key concepts of QML](https://pennylane.ai/qml/glossary.html) +* [QML videos](https://pennylane.ai/qml/videos.html) + +You can also check out our [documentation](https://pennylane.readthedocs.io) for [quickstart +guides](https://pennylane.readthedocs.io/en/stable/introduction/pennylane.html) to using PennyLane, +and detailed developer guides on [how to write your +own](https://pennylane.readthedocs.io/en/stable/development/plugins.html) PennyLane-compatible +quantum device. + +## Tutorials and demonstrations + +Take a deeper dive into quantum machine learning by exploring cutting-edge algorithms on our [demonstrations +page](https://pennylane.ai/qml/demonstrations.html). + +<a href="https://pennylane.ai/qml/demonstrations.html"> + <img src="https://raw.githubusercontent.com/PennyLaneAI/pennylane/master/doc/_static/readme/demos.png" width="900px"> +</a> + +All demonstrations are fully executable, and can be downloaded as Jupyter notebooks and Python +scripts. + +If you would like to contribute your own demo, see our [demo submission +guide](https://pennylane.ai/qml/demos_submission.html). + +## Videos + +Seeing is believing! Check out [our videos](https://pennylane.ai/qml/videos.html) to learn about +PennyLane, quantum computing concepts, and more. + +<a href="https://pennylane.ai/qml/videos.html"> + <img src="https://raw.githubusercontent.com/PennyLaneAI/pennylane/master/doc/_static/readme/videos.png" width="900px"> +</a> + +## Contributing to PennyLane + +We welcome contributions—simply fork the PennyLane repository, and then make a [pull +request](https://help.github.com/articles/about-pull-requests/) containing your contribution. All +contributors to PennyLane will be listed as authors on the releases. All users who contribute +significantly to the code (new plugins, new functionality, etc.) will be listed on the PennyLane +arXiv paper. + +We also encourage bug reports, suggestions for new features and enhancements, and even links to cool +projects or applications built on PennyLane. + +See our [contributions +page](https://github.com/PennyLaneAI/pennylane/blob/master/.github/CONTRIBUTING.md) and our +[developer hub](https://pennylane.readthedocs.io/en/stable/development/guide.html) for more +details. + +## Support + +- **Source Code:** https://github.com/PennyLaneAI/pennylane +- **Issue Tracker:** https://github.com/PennyLaneAI/pennylane/issues + +If you are having issues, please let us know by posting the issue on our GitHub issue tracker. + +We also have a [PennyLane discussion forum](https://discuss.pennylane.ai)—come join the community +and chat with the PennyLane team. + +Note that we are committed to providing a friendly, safe, and welcoming environment for all. +Please read and respect the [Code of Conduct](.github/CODE_OF_CONDUCT.md). + +## Authors + +PennyLane is the work of [many contributors](https://github.com/PennyLaneAI/pennylane/graphs/contributors). + +If you are doing research using PennyLane, please cite [our paper](https://arxiv.org/abs/1811.04968): + +> Ville Bergholm et al. *PennyLane: Automatic differentiation of hybrid quantum-classical +> computations.* 2018. arXiv:1811.04968 + +## License + +PennyLane is **free** and **open source**, released under the Apache License, Version 2.0. + + +%package help +Summary: Development documents and examples for PennyLane +Provides: python3-PennyLane-doc +%description help +<p align="center"> + <!-- Tests (GitHub actions) --> + <a href="https://github.com/PennyLaneAI/pennylane/actions?query=workflow%3ATests"> + <img src="https://img.shields.io/github/actions/workflow/status/PennyLaneAI/PennyLane/tests.yml?branch=master&style=flat-square" /> + </a> + <!-- CodeCov --> + <a href="https://codecov.io/gh/PennyLaneAI/pennylane"> + <img src="https://img.shields.io/codecov/c/github/PennyLaneAI/pennylane/master.svg?logo=codecov&style=flat-square" /> + </a> + <!-- ReadTheDocs --> + <a href="https://docs.pennylane.ai/en/latest"> + <img src="https://readthedocs.com/projects/xanaduai-pennylane/badge/?version=latest&style=flat-square" /> + </a> + <!-- PyPI --> + <a href="https://pypi.org/project/PennyLane"> + <img src="https://img.shields.io/pypi/v/PennyLane.svg?style=flat-square" /> + </a> + <!-- Forum --> + <a href="https://discuss.pennylane.ai"> + <img src="https://img.shields.io/discourse/https/discuss.pennylane.ai/posts.svg?logo=discourse&style=flat-square" /> + </a> + <!-- License --> + <a href="https://www.apache.org/licenses/LICENSE-2.0"> + <img src="https://img.shields.io/pypi/l/PennyLane.svg?logo=apache&style=flat-square" /> + </a> +</p> + +<p align="center"> + <a href="https://pennylane.ai">PennyLane</a> is a cross-platform Python library for <a + href="https://en.wikipedia.org/wiki/Differentiable_programming">differentiable + programming</a> of quantum computers. +</p> + +<p align="center"> + <strong>Train a quantum computer the same way as a neural network.</strong> + <img src="https://raw.githubusercontent.com/PennyLaneAI/pennylane/master/doc/_static/header.png#gh-light-mode-only" width="700px"> + <!-- + Use a relative import for the dark mode image. When loading on PyPI, this + will fail automatically and show nothing. + --> + <img src="./doc/_static/header-dark-mode.png#gh-dark-mode-only" width="700px" onerror="this.style.display='none'" alt=""/> +</p> + +## Key Features + +<img src="https://raw.githubusercontent.com/PennyLaneAI/pennylane/master/doc/_static/code.png" width="400px" align="right"> + +- *Machine learning on quantum hardware*. Connect to quantum hardware using **PyTorch**, **TensorFlow**, **JAX**, **Keras**, or **NumPy**. Build rich and flexible hybrid quantum-classical models. + +- *Device-independent*. Run the same quantum circuit on different quantum backends. Install + [plugins](https://pennylane.ai/plugins.html) to access even more devices, including **Strawberry + Fields**, **Amazon Braket**, **IBM Q**, **Google Cirq**, **Rigetti Forest**, **Qulacs**, **Pasqal**, **Honeywell**, and more. + +- *Follow the gradient*. Hardware-friendly **automatic differentiation** of quantum circuits. + +- *Batteries included*. Built-in tools for **quantum machine learning**, **optimization**, and + **quantum chemistry**. Rapidly prototype using built-in quantum simulators with + backpropagation support. + +## Installation + +PennyLane requires Python version 3.8 and above. Installation of PennyLane, as well as all +dependencies, can be done using pip: + +```console +python -m pip install pennylane +``` + +## Docker support + +**Docker** support exists for building using **CPU** and **GPU** (Nvidia CUDA +11.1+) images. [See a more detailed description +here](https://pennylane.readthedocs.io/en/stable/development/guide/installation.html#docker). + +## Getting started + +For an introduction to quantum machine learning, guides and resources are available on +PennyLane's [quantum machine learning hub](https://pennylane.ai/qml/): + +<img src="https://raw.githubusercontent.com/PennyLaneAI/pennylane/master/doc/_static/readme/gpu_to_qpu.png" align="right" width="400px"> + +* [What is quantum machine learning?](https://pennylane.ai/qml/whatisqml.html) +* [QML tutorials and demos](https://pennylane.ai/qml/demonstrations.html) +* [Frequently asked questions](https://pennylane.ai/faq.html) +* [Key concepts of QML](https://pennylane.ai/qml/glossary.html) +* [QML videos](https://pennylane.ai/qml/videos.html) + +You can also check out our [documentation](https://pennylane.readthedocs.io) for [quickstart +guides](https://pennylane.readthedocs.io/en/stable/introduction/pennylane.html) to using PennyLane, +and detailed developer guides on [how to write your +own](https://pennylane.readthedocs.io/en/stable/development/plugins.html) PennyLane-compatible +quantum device. + +## Tutorials and demonstrations + +Take a deeper dive into quantum machine learning by exploring cutting-edge algorithms on our [demonstrations +page](https://pennylane.ai/qml/demonstrations.html). + +<a href="https://pennylane.ai/qml/demonstrations.html"> + <img src="https://raw.githubusercontent.com/PennyLaneAI/pennylane/master/doc/_static/readme/demos.png" width="900px"> +</a> + +All demonstrations are fully executable, and can be downloaded as Jupyter notebooks and Python +scripts. + +If you would like to contribute your own demo, see our [demo submission +guide](https://pennylane.ai/qml/demos_submission.html). + +## Videos + +Seeing is believing! Check out [our videos](https://pennylane.ai/qml/videos.html) to learn about +PennyLane, quantum computing concepts, and more. + +<a href="https://pennylane.ai/qml/videos.html"> + <img src="https://raw.githubusercontent.com/PennyLaneAI/pennylane/master/doc/_static/readme/videos.png" width="900px"> +</a> + +## Contributing to PennyLane + +We welcome contributions—simply fork the PennyLane repository, and then make a [pull +request](https://help.github.com/articles/about-pull-requests/) containing your contribution. All +contributors to PennyLane will be listed as authors on the releases. All users who contribute +significantly to the code (new plugins, new functionality, etc.) will be listed on the PennyLane +arXiv paper. + +We also encourage bug reports, suggestions for new features and enhancements, and even links to cool +projects or applications built on PennyLane. + +See our [contributions +page](https://github.com/PennyLaneAI/pennylane/blob/master/.github/CONTRIBUTING.md) and our +[developer hub](https://pennylane.readthedocs.io/en/stable/development/guide.html) for more +details. + +## Support + +- **Source Code:** https://github.com/PennyLaneAI/pennylane +- **Issue Tracker:** https://github.com/PennyLaneAI/pennylane/issues + +If you are having issues, please let us know by posting the issue on our GitHub issue tracker. + +We also have a [PennyLane discussion forum](https://discuss.pennylane.ai)—come join the community +and chat with the PennyLane team. + +Note that we are committed to providing a friendly, safe, and welcoming environment for all. +Please read and respect the [Code of Conduct](.github/CODE_OF_CONDUCT.md). + +## Authors + +PennyLane is the work of [many contributors](https://github.com/PennyLaneAI/pennylane/graphs/contributors). + +If you are doing research using PennyLane, please cite [our paper](https://arxiv.org/abs/1811.04968): + +> Ville Bergholm et al. *PennyLane: Automatic differentiation of hybrid quantum-classical +> computations.* 2018. arXiv:1811.04968 + +## License + +PennyLane is **free** and **open source**, released under the Apache License, Version 2.0. + + +%prep +%autosetup -n PennyLane-0.29.1 + +%build +%py3_build + +%install +%py3_install +install -d -m755 %{buildroot}/%{_pkgdocdir} +if [ -d doc ]; then cp -arf doc %{buildroot}/%{_pkgdocdir}; fi +if [ -d docs ]; then cp -arf docs %{buildroot}/%{_pkgdocdir}; fi +if [ -d example ]; then cp -arf example %{buildroot}/%{_pkgdocdir}; fi +if [ -d examples ]; then cp -arf examples %{buildroot}/%{_pkgdocdir}; fi +pushd %{buildroot} +if [ -d usr/lib ]; then + find usr/lib -type f -printf "/%h/%f\n" >> filelist.lst +fi +if [ -d usr/lib64 ]; then + find usr/lib64 -type f -printf "/%h/%f\n" >> filelist.lst +fi +if [ -d usr/bin ]; then + find usr/bin -type f -printf "/%h/%f\n" >> filelist.lst +fi +if [ -d usr/sbin ]; then + find usr/sbin -type f -printf "/%h/%f\n" >> filelist.lst +fi +touch doclist.lst +if [ -d usr/share/man ]; then + find usr/share/man -type f -printf "/%h/%f.gz\n" >> doclist.lst +fi +popd +mv %{buildroot}/filelist.lst . +mv %{buildroot}/doclist.lst . + +%files -n python3-PennyLane -f filelist.lst +%dir %{python3_sitelib}/* + +%files help -f doclist.lst +%{_docdir}/* + +%changelog +* Tue Apr 11 2023 Python_Bot <Python_Bot@openeuler.org> - 0.29.1-1 +- Package Spec generated |