.. image:: doc/_static/pennylane_thin.png
:alt: PennyLane

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:target: https://codecov.io/gh/XanaduAI/pennylane

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:target: https://app.codacy.com/app/XanaduAI/pennylane?utm_source=github.com&utm_medium=referral&utm_content=XanaduAI/pennylane&utm_campaign=badger

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:target: https://www.apache.org/licenses/LICENSE-2.0, CI, COV, PEP, DOC, VERS, PY, FORUM, 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

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<img src="https://raw.githubusercontent.com/XanaduAI/pennylane/master/doc/_static/code.png" width="300px"  align="right">

• 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.