PennyLane-Forest 0.24.0
Rigetti backend for the PennyLane library
License: BSD 3-Clause

PennyLane Forest Plugin
#######################

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    :target: https://github.com/PennyLaneAI/pennylane-forest/actions?query=workflow%3ATests

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    :alt: Codecov coverage
    :target: https://codecov.io/gh/PennyLaneAI/pennylane-forest

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    :alt: Read the Docs
    :target: https://pennylaneforest.readthedocs.io

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    :alt: PyPI
    :target: https://pypi.org/project/pennylane-forest

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    :alt: PyPI - Python Version
    :target: https://pypi.org/project/pennylane-forest

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The PennyLane Forest plugin allows different Rigetti devices to work with
PennyLane --- the wavefunction simulator, and the Quantum Virtual Machine (QVM).

`pyQuil <https://pyquil.readthedocs.io>`__ is a Python library for quantum programming using the
quantum instruction language (Quil) --- resulting quantum programs can be executed using the
`Rigetti Forest SDK <https://pyquil-docs.rigetti.com/en/stable/>`__ and the `Rigetti QCS
<https://qcs.rigetti.com/>`__.

`PennyLane <https://pennylane.readthedocs.io>`__ is a cross-platform Python library for quantum machine
learning, automatic differentiation, and optimization of hybrid quantum-classical computations.


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The plugin documentation can be found here: `<https://pennylane-forest.readthedocs.io/en/latest/>`__.

Features
========

* Provides three devices to be used with PennyLane: ``forest.numpy_wavefunction``,
  ``forest.wavefunction``, and ``forest.qvm``. These provide access to the pyQVM
  Numpy wavefunction simulator, Forest wavefunction simulator, and quantum
  virtual machine (QVM) respectively.


* All provided devices support all core qubit PennyLane operations and observables.


* Provides custom PennyLane operations to cover additional pyQuil operations:
  ``ISWAP``, ``PSWAP``, and ``CPHASE``. Every custom operation supports analytic
  differentiation.

* Combine Forest and the Rigetti Cloud Services with PennyLane's automatic differentiation and
  optimization.


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Installation
============

PennyLane-Forest, as well as all required Python packages mentioned above, can be installed via ``pip``:
::

   	$ python -m pip install pennylane-forest


Make sure you are using the Python 3 version of pip.

Alternatively, you can install PennyLane-Forest from the source code by navigating to the top-level directory and running
::

	$ python setup.py install

Dependencies
~~~~~~~~~~~~

PennyLane-Forest requires the following libraries be installed:

* `Python <http://python.org/>`__ >=3.6

as well as the following Python packages:

* `PennyLane <http://pennylane.readthedocs.io/>`__
* `pyQuil <https://pyquil-docs.rigetti.com/en/stable/>`__ >=2.16, <2.28.3

Note that the latest PyQuil version 3.0 is not currently supported.

If you currently do not have Python 3 installed, we recommend
`Anaconda for Python 3 <https://www.anaconda.com/download/>`__, a distributed version
of Python packaged for scientific computation.

Additionally, if you would like to compile the quantum instruction language (Quil) and run it
locally using a quantum virtual machine (QVM) server, you will need to download and install the
Forest software development kit (SDK):

* `Forest SDK <https://pyquil-docs.rigetti.com/en/stable/>`__

Alternatively, you may sign up for Rigetti's Quantum Cloud Services (QCS) to acquire a Quantum Machine
Image (QMI) which will allow you to compile your quantum code and run on real quantum processing units (QPUs),
or on a preinstalled QVM. Note that this requires a valid QCS account.

* `Quantum Cloud Services <https://docs.rigetti.com/en/>`__

Tests
~~~~~

To test that the PennyLane-Forest plugin is working correctly you can run

.. code-block:: bash

    $ make test

in the source folder.

Documentation
~~~~~~~~~~~~~

To build the HTML documentation, go to the top-level directory and run:

.. code-block:: bash

  $ make docs


The documentation can then be found in the ``doc/_build/html/`` directory.

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Contributing
============

We welcome contributions - simply fork the repository of this plugin, and then make a
`pull request <https://help.github.com/articles/about-pull-requests/>`__ containing your contribution.
All contributers to this plugin will be listed as authors on the releases.

We also encourage bug reports, suggestions for new features and enhancements, and even links to cool projects
or applications built on PennyLane.


Authors
=======

PennyLane-Forest is the work of `many contributors <https://github.com/PennyLaneAI/pennylane-forest/graphs/contributors>`__.

If you are doing research using PennyLane and PennyLane-Forest, 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

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Support
=======

- **Source Code:** https://github.com/PennyLaneAI/pennylane-forest
- **Issue Tracker:** https://github.com/PennyLaneAI/pennylane-forest/issues
- **PennyLane Forum:** https://discuss.pennylane.ai

If you are having issues, please let us know by posting the issue on our Github issue tracker, or
by asking a question in the forum.

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License
=======

PennyLane-Forest is **free** and **open source**, released under the BSD 3-Clause `license
<https://github.com/PennyLaneAI/pennylane-forest/blob/master/LICENSE>`__.

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