PennyLane-IonQ Plugin
#####################
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.. image:: https://readthedocs.com/projects/xanaduai-pennylane-ionq/badge/?version=latest&style=flat-square
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.. image:: https://img.shields.io/pypi/v/PennyLane-ionq.svg?style=flat-square
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:alt: PyPI - Python Version
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.. header-start-inclusion-marker-do-not-remove
The PennyLane-IonQ plugin provides the ability to use IonQ's ion-trap
quantum computing backends with PennyLane.
`PennyLane <https://pennylane.ai>`_ provides open-source tools for
quantum machine learning, quantum computing, quantum chemistry, and hybrid quantum-classical computing.
`IonQ <https://www.ionq.com>`_ is a ion-trap quantum computing
company offering access to quantum computing devices over the cloud.
.. header-end-inclusion-marker-do-not-remove
The plugin documentation can be found `here <https://docs.pennylane.ai/projects/ionq>`__.
Features
========
* Provides two devices which can be used with IonQ's online API: ``"ionq.simulator"`` and ``"ionq.qpu"``.
These provide access to an ideal ion-trap simulator as well as IonQ's quantum hardware, respectively.
* The plugin provides additional support for the IonQ's Ising-type gates.
* Supports core PennyLane operations such as qubit rotations, Hadamard, basis state preparations, etc.
.. installation-start-inclusion-marker-do-not-remove
Installation
============
PennyLane-IonQ only requires PennyLane for use, no additional external frameworks are needed.
The plugin can be installed via ``pip``:
::
$ python3 -m pip install pennylane-ionq
Alternatively, you can install PennyLane-IonQ from the source code by navigating to the top directory and running
::
$ python3 setup.py install
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.
Software tests
~~~~~~~~~~~~~~
To ensure that PennyLane-IonQ is working correctly after installation, the test suite can be
run by navigating to the source code folder and running
::
$ make test
Documentation
~~~~~~~~~~~~~
To build the HTML documentation, go to the top-level directory and run
::
$ make docs
The documentation can then be found in the ``doc/_build/html/`` directory.
.. installation-end-inclusion-marker-do-not-remove
Getting started
===============
Once PennyLane is installed, the provided IonQ devices can be accessed straight
away in PennyLane. However, the user will need access credentials for the IonQ platform in order to
use these remote devices. These credentials should be provided to PennyLane via a
`configuration file or environment variable <https://pennylane.readthedocs.io/en/stable/introduction/configuration.html>`_.
Specifically, the variable ``IONQ_API_KEY`` must contain a valid access key for IonQ's online platform.
You can instantiate the IonQ devices for PennyLane as follows:
.. code-block:: python
import pennylane as qml
dev1 = qml.device('ionq.simulator', wires=2, shots=1000)
dev2 = qml.device('ionq.qpu', backend='aria-1', wires=2, shots=1000)
These devices can then be used just like other devices for the definition and evaluation of
quantum circuits within PennyLane. For more details and ideas, see the
`PennyLane website <https://pennylane.ai>`_ and refer
to the `PennyLane documentation <https://pennylane.readthedocs.io>`_.
Contributing
============
We welcome contributions-simply fork the PennyLane-IonQ repository, and then make a
`pull request <https://help.github.com/articles/about-pull-requests/>`_ containing your contribution.
All contributers to PennyLane-IonQ will be listed as contributors 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 and IonQ.
Contributors
============
PennyLane-IonQ is the work of many `contributors <https://github.com/PennyLaneAI/pennylane-ionq/graphs/contributors>`_.
If you are doing research using PennyLane, please cite our papers:
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, Nathan Killoran.
*PennyLane: Automatic differentiation of hybrid quantum-classical computations.* 2018.
`arXiv:1811.04968 <https://arxiv.org/abs/1811.04968>`_
Maria Schuld, Ville Bergholm, Christian Gogolin, Josh Izaac, and Nathan Killoran.
*Evaluating analytic gradients on quantum hardware.* 2018.
`Phys. Rev. A 99, 032331 <https://journals.aps.org/pra/abstract/10.1103/PhysRevA.99.032331>`_
.. support-start-inclusion-marker-do-not-remove
Support
=======
- **Source Code:** https://github.com/PennyLaneAI/pennylane-ionq
- **Issue Tracker:** https://github.com/PennyLaneAI/pennylane-ionq/issues
If you are having issues, please let us know by posting the issue on our GitHub issue tracker.
.. support-end-inclusion-marker-do-not-remove
.. license-start-inclusion-marker-do-not-remove
License
=======
PennyLane-IonQ is **free** and **open source**, released under the Apache License, Version 2.0.
.. license-end-inclusion-marker-do-not-remove
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"description": "PennyLane-IonQ Plugin\n#####################\n\n.. image:: https://img.shields.io/github/actions/workflow/status/PennyLaneAI/pennylane-ionq/tests.yml?branch=master&logo=github&style=flat-square\n :alt: GitHub Workflow Status (branch)\n :target: https://github.com/PennyLaneAI/pennylane-ionq/actions?query=workflow%3ATests\n\n.. image:: https://img.shields.io/codecov/c/github/PennyLaneAI/pennylane-ionq/master.svg?logo=codecov&style=flat-square\n :alt: Codecov coverage\n :target: https://codecov.io/gh/PennyLaneAI/pennylane-ionq\n\n.. image:: https://img.shields.io/codefactor/grade/github/PennyLaneAI/pennylane-ionq/master?logo=codefactor&style=flat-square\n :alt: CodeFactor Grade\n :target: https://www.codefactor.io/repository/github/pennylaneai/pennylane-ionq\n\n.. image:: https://readthedocs.com/projects/xanaduai-pennylane-ionq/badge/?version=latest&style=flat-square\n :alt: Read the Docs\n :target: https://docs.pennylane.ai/projects/ionq\n\n.. image:: https://img.shields.io/pypi/v/PennyLane-ionq.svg?style=flat-square\n :alt: PyPI\n :target: https://pypi.org/project/PennyLane-ionq\n\n.. image:: https://img.shields.io/pypi/pyversions/PennyLane-ionq.svg?style=flat-square\n :alt: PyPI - Python Version\n :target: https://pypi.org/project/PennyLane-ionq\n\n.. header-start-inclusion-marker-do-not-remove\n\nThe PennyLane-IonQ plugin provides the ability to use IonQ's ion-trap\nquantum computing backends with PennyLane.\n\n`PennyLane <https://pennylane.ai>`_ provides open-source tools for\nquantum machine learning, quantum computing, quantum chemistry, and hybrid quantum-classical computing.\n\n`IonQ <https://www.ionq.com>`_ is a ion-trap quantum computing\ncompany offering access to quantum computing devices over the cloud.\n\n.. header-end-inclusion-marker-do-not-remove\n\nThe plugin documentation can be found `here <https://docs.pennylane.ai/projects/ionq>`__.\n\nFeatures\n========\n\n* Provides two devices which can be used with IonQ's online API: ``\"ionq.simulator\"`` and ``\"ionq.qpu\"``.\n These provide access to an ideal ion-trap simulator as well as IonQ's quantum hardware, respectively.\n\n* The plugin provides additional support for the IonQ's Ising-type gates.\n\n* Supports core PennyLane operations such as qubit rotations, Hadamard, basis state preparations, etc.\n\n.. installation-start-inclusion-marker-do-not-remove\n\nInstallation\n============\n\nPennyLane-IonQ only requires PennyLane for use, no additional external frameworks are needed.\nThe plugin can be installed via ``pip``:\n::\n\n $ python3 -m pip install pennylane-ionq\n\nAlternatively, you can install PennyLane-IonQ from the source code by navigating to the top directory and running\n::\n\n $ python3 setup.py install\n\n\nIf you currently do not have Python 3 installed,\nwe recommend `Anaconda for Python 3 <https://www.anaconda.com/download/>`_, a distributed\nversion of Python packaged for scientific computation.\n\nSoftware tests\n~~~~~~~~~~~~~~\n\nTo ensure that PennyLane-IonQ is working correctly after installation, the test suite can be\nrun by navigating to the source code folder and running\n::\n\n $ make test\n\n\nDocumentation\n~~~~~~~~~~~~~\n\nTo build the HTML documentation, go to the top-level directory and run\n::\n\n $ make docs\n\nThe documentation can then be found in the ``doc/_build/html/`` directory.\n\n.. installation-end-inclusion-marker-do-not-remove\n\nGetting started\n===============\n\nOnce PennyLane is installed, the provided IonQ devices can be accessed straight\naway in PennyLane. However, the user will need access credentials for the IonQ platform in order to\nuse these remote devices. These credentials should be provided to PennyLane via a\n`configuration file or environment variable <https://pennylane.readthedocs.io/en/stable/introduction/configuration.html>`_.\nSpecifically, the variable ``IONQ_API_KEY`` must contain a valid access key for IonQ's online platform.\n\nYou can instantiate the IonQ devices for PennyLane as follows:\n\n.. code-block:: python\n\n import pennylane as qml\n dev1 = qml.device('ionq.simulator', wires=2, shots=1000)\n dev2 = qml.device('ionq.qpu', backend='aria-1', wires=2, shots=1000)\n\nThese devices can then be used just like other devices for the definition and evaluation of\nquantum circuits within PennyLane. For more details and ideas, see the\n`PennyLane website <https://pennylane.ai>`_ and refer\nto the `PennyLane documentation <https://pennylane.readthedocs.io>`_.\n\n\nContributing\n============\n\nWe welcome contributions-simply fork the PennyLane-IonQ repository, and then make a\n`pull request <https://help.github.com/articles/about-pull-requests/>`_ containing your contribution.\nAll contributers to PennyLane-IonQ will be listed as contributors on the releases.\n\nWe also encourage bug reports, suggestions for new features and enhancements, and even links to cool\nprojects or applications built on PennyLane and IonQ.\n\n\nContributors\n============\n\nPennyLane-IonQ is the work of many `contributors <https://github.com/PennyLaneAI/pennylane-ionq/graphs/contributors>`_.\n\nIf you are doing research using PennyLane, please cite our papers:\n\n Ville Bergholm, Josh Izaac, Maria Schuld, Christian Gogolin, M. Sohaib Alam, Shahnawaz Ahmed,\n Juan Miguel Arrazola, Carsten Blank, Alain Delgado, Soran Jahangiri, Keri McKiernan, Johannes Jakob Meyer,\n Zeyue Niu, Antal Sz\u00e1va, Nathan Killoran.\n *PennyLane: Automatic differentiation of hybrid quantum-classical computations.* 2018.\n `arXiv:1811.04968 <https://arxiv.org/abs/1811.04968>`_\n\n Maria Schuld, Ville Bergholm, Christian Gogolin, Josh Izaac, and Nathan Killoran.\n *Evaluating analytic gradients on quantum hardware.* 2018.\n `Phys. Rev. A 99, 032331 <https://journals.aps.org/pra/abstract/10.1103/PhysRevA.99.032331>`_\n\n.. support-start-inclusion-marker-do-not-remove\n\nSupport\n=======\n\n- **Source Code:** https://github.com/PennyLaneAI/pennylane-ionq\n- **Issue Tracker:** https://github.com/PennyLaneAI/pennylane-ionq/issues\n\nIf you are having issues, please let us know by posting the issue on our GitHub issue tracker.\n\n.. support-end-inclusion-marker-do-not-remove\n.. license-start-inclusion-marker-do-not-remove\n\nLicense\n=======\n\nPennyLane-IonQ is **free** and **open source**, released under the Apache License, Version 2.0.\n\n.. license-end-inclusion-marker-do-not-remove\n",
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