Metadata-Version: 2.1
Name: PennyLane
Version: 0.19.1
Summary: PennyLane is a Python quantum machine learning library by Xanadu Inc.
Home-page: https://github.com/XanaduAI/pennylane
Maintainer: Xanadu Inc.
Maintainer-email: software@xanadu.ai
License: Apache License 2.0
Description: <p align="center">
          <a href="https://pennylane.ai">
            <img width=80% src="https://raw.githubusercontent.com/PennyLaneAI/pennylane/master/doc/_static/pennylane_thin.png">
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        </p>
        
        <p align="center">
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        </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" width="700px">
        </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 3.7 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#installation).
        
        ## 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).
        
        ## 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, Josh Izaac, Maria Schuld, Christian Gogolin, M. Sohaib Alam, Shahnawaz Ahmed,
        > Juan Miguel Arrazola, Carsten Blank, Alain Delgado, Soran Jahangiri, Keri McKiernan, Johannes Jakob Meyer,
        > Zeyue Niu, Antal Száva, and Nathan Killoran.
        > *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.
        
Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
Classifier: Environment :: Console
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Natural Language :: English
Classifier: Operating System :: POSIX
Classifier: Operating System :: MacOS :: MacOS X
Classifier: Operating System :: POSIX :: Linux
Classifier: Operating System :: Microsoft :: Windows
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3 :: Only
Classifier: Topic :: Scientific/Engineering :: Physics
Provides: pennylane
Description-Content-Type: text/markdown
Provides-Extra: kernels
