Metadata-Version: 2.1
Name: PennyLane
Version: 0.12.0
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: .. image:: doc/_static/pennylane_thin.png
            :alt: PennyLane
        
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        `PennyLane <https://pennylane.ai>`_ is a cross-platform Python library for `differentiable programming <https://en.wikipedia.org/wiki/Differentiable_programming>`__ of quantum computers.
        
        PennyLane provides open-source tools for quantum machine learning, quantum computing, quantum chemistry, and hybrid quantum-classical computing. Extensive examples, tutorials, and demos are available at https://pennylane.ai/qml.
        
        Key Features
        ============
        
        - *Device independent*.
          Access quantum hardware and simulators from **Xanadu Strawberry Fields**, **IBM Q**, **Google Cirq**, **Rigetti Forest**, and
          **Microsoft QDK**.
        
        - *Best of both worlds*.
          Build hybrid models by connecting quantum hardware to **PyTorch**, **TensorFlow**, **Keras**, and **NumPy**.
        
        - *Follow the gradient*. Hardware-friendly **automatic differentiation** of quantum circuits.
        
        - *Batteries included*. Built-in tools for **quantum machine learning**, **optimization**, and **quantum chemistry**.
        
        Getting started
        ===============
        
        For an introduction to quantum machine learning, we have several guides and resources available
        on PennyLane's `quantum machine learning page <https://pennylane.ai/qml/>`_:
        
        * `What is quantum machine learning? <https://pennylane.ai/qml/whatisqml.html>`_
        * `QML tutorials and demonstrations <https://pennylane.ai/qml/demonstrations.html>`_
        * `Frequently asked questions <https://pennylane.ai/faq.html>`_
        * `Glossary of key concepts <https://pennylane.ai/qml/glossary.html>`_
        * `Curated selection of 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.
        
        Available plugins
        =================
        
        * `PennyLane-SF <https://github.com/PennyLaneAI/pennylane-sf>`_: Supports integration with
          `Strawberry Fields <https://github.com/PennyLaneAI/strawberryfields>`__, a full-stack
          Python library for simulating photonic quantum computing.
        
        
        * `PennyLane-qiskit <https://github.com/PennyLaneAI/pennylane-qiskit>`_: Supports
          integration with `Qiskit <https://qiskit.org>`__, an open-source quantum
          computation framework by IBM. Provides device support for the Qiskit Aer quantum
          simulators, and IBM Q hardware devices.
        
        
        * `PennyLane-cirq <https://github.com/PennyLaneAI/pennylane-cirq>`_: Supports
          integration with `Cirq <https://github.com/quantumlib/cirq>`__, an open-source quantum
          computation framework by Google.
        
        
        * `PennyLane-Forest <https://github.com/rigetti/pennylane-forest>`_: Supports integration
          with `PyQuil <https://github.com/rigetti/pyquil>`__, the
          `Rigetti Forest SDK <https://www.rigetti.com/forest>`__, and the
          `Rigetti QCS <https://www.rigetti.com/qcs>`__, an open-source quantum computation
          framework by Rigetti. Provides device support for the the Quantum Virtual Machine
          (QVM) and Quantum Processing Units (QPUs) hardware devices.
        
        
        * `PennyLane-Qsharp <https://github.com/PennyLaneAI/pennylane-qsharp>`_: Supports integration
          with the `Microsoft Quantum Development Kit <https://www.microsoft.com/en-us/quantum/development-kit>`__,
          a quantum computation framework that uses the Q# quantum programming language.
        
        
        For a full list of PennyLane plugins, see `the PennyLane website <https://pennylane.ai/plugins.html>`__.
        
        Installation
        ============
        
        PennyLane requires Python version 3.6 and above. Installation of PennyLane, as well
        as all dependencies, can be done using pip:
        
        .. code-block:: bash
        
            $ python -m pip install pennylane
        
        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 contributers 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>`_
        for more details.
        
        
        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
        
        
        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 discussion and chat with our PennyLane team.
        
        
        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 :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3 :: Only
Classifier: Topic :: Scientific/Engineering :: Physics
Provides: pennylane
Description-Content-Type: text/x-rst
