Metadata-Version: 1.2
Name: PennyLane-IonQ
Version: 0.15.0
Summary: PennyLane plugin for IonQ
Home-page: http://xanadu.ai
Maintainer: Xanadu Inc.
Maintainer-email: software@xanadu.ai
License: Apache License 2.0
Description: PennyLane-IonQ Plugin
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            :target: https://pypi.org/project/PennyLane-ionq
        
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        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.
        
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        The plugin documentation can be found `here <https://pennylane-ionq.readthedocs.io/en/latest/>`__.
        
        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.
        
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        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.
        
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        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', 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>`_
        
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        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.
        
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        License
        =======
        
        PennyLane-IonQ is **free** and **open source**, released under the Apache License, Version 2.0.
        
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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 :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3 :: Only
Classifier: Topic :: Scientific/Engineering :: Physics
Provides: pennylane_ionq
