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+/PennyLane-0.29.1.tar.gz
diff --git a/python-pennylane.spec b/python-pennylane.spec
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+%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
diff --git a/sources b/sources
new file mode 100644
index 0000000..a78144c
--- /dev/null
+++ b/sources
@@ -0,0 +1 @@
+f5d364ad441a30ee8c0bcd5e0e34f3b6  PennyLane-0.29.1.tar.gz