Metadata-Version: 1.2
Name: PennyLane
Version: 0.8.1
Summary: PennyLane is a Python quantum machine learning library by Xanadu Inc.
Home-page: https://github.com/XanaduAI/pennylane
Maintainer: Xanadu Inc.
Maintainer-email: nathan@xanadu.ai
License: Apache License 2.0
Description: .. image:: doc/_static/pennylane_thin.png
            :alt: PennyLane
        
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        `PennyLane <https://pennylane.readthedocs.io>`_ is a cross-platform Python library for quantum machine learning,
        automatic differentiation, and optimization of hybrid quantum-classical computations.
        
        Learn more about quantum machine learning with PennyLane: view and download QML examples
        and demos over at https://pennylane.ai/qml.
        
        Features
        ========
        
        - **Follow the gradient**. Built-in **automatic differentiation** of quantum circuits
        
        - **Best of both worlds**.
          Support for **hybrid quantum and classical** models; connect quantum
          hardware with PyTorch, TensorFlow, and NumPy.
        
        - **Batteries included**. Provides **optimization and machine learning** tools
        
        - **Device independent**.
          The same quantum circuit model can be **run on different backends**. Install
          `plugins <https://pennylane.ai/plugins.html>`__ to access even more
          devices, including **Strawberry Fields**, **IBM Q**, **Google Cirq**, **Rigetti Forest**, and
          **Microsoft QDK**.
        
        Available plugins
        =================
        
        * `PennyLane-SF <https://github.com/XanaduAI/pennylane-sf>`_: Supports integration with
          `Strawberry Fields <https://github.com/XanaduAI/strawberryfields>`__, a full-stack
          Python library for simulating continuous variable (CV) quantum optical circuits.
        
        
        * `PennyLane-qiskit <https://github.com/XanaduAI/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/XanaduAI/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/XanaduAI/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 3.5 and above. Installation of PennyLane, as well
        as all dependencies, can be done using pip:
        
        .. code-block:: bash
        
            $ python -m pip install pennylane
        
        
        Getting started
        ===============
        
        For getting started with PennyLane, check out some of the
        `key concepts <https://pennylane.ai/qml/concepts.html>`_ behind quantum machine
        learning, before moving on to some `introductory tutorials <https://pennylane.ai/qml/beginner.html>`_.
        
        Then, take a deeper dive into quantum machine learning by
        exploring cutting-edge algorithms using PennyLane and near-term quantum hardware,
        with our collection of
        `QML tutorials <https://pennylane.ai/qml/implementations.html>`_.
        
        You can also check out our `documentation <https://pennylane.readthedocs.io>`_ for
        more details on the quantum operations, and to explore the available optimization
        tools provided by PennyLane, and detailed guides on
        `how to write your own <https://pennylane.readthedocs.io/en/latest/development/plugins.html>`_
        PennyLane-compatible quantum device.
        
        Finally, play around with the numerous `devices and plugins <https://pennylane.ai/plugins.html>`_
        available for running your hybrid optimizations — these include
        IBM Q, provided by the PennyLane-Qiskit plugin, as well as the Rigetti Aspen-1 QPU.
        
        
        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/XanaduAI/pennylane/blob/master/.github/CONTRIBUTING.md>`_
        for more details.
        
        
        Authors
        =======
        
        Ville Bergholm, Josh Izaac, Maria Schuld, Christian Gogolin, and Nathan Killoran.
        
        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, Carsten Blank, Keri McKiernan, and Nathan Killoran.
            *PennyLane: Automatic differentiation of hybrid quantum-classical computations.* 2018. arXiv:1811.04968
        
        
        Support
        =======
        
        - **Source Code:** https://github.com/XanaduAI/pennylane
        - **Issue Tracker:** https://github.com/XanaduAI/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 :: 3.5
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
