{ "info": { "author": "", "author_email": "", "bugtrack_url": null, "classifiers": [ "Development Status :: 4 - Beta", "Environment :: Console", "Intended Audience :: Science/Research", "License :: OSI Approved :: Apache Software License", "Natural Language :: English", "Operating System :: MacOS :: MacOS X", "Operating System :: Microsoft :: Windows", "Operating System :: POSIX", "Operating System :: POSIX :: Linux", "Programming Language :: Python", "Programming Language :: Python :: 3", "Programming Language :: Python :: 3.5", "Programming Language :: Python :: 3.6", "Programming Language :: Python :: 3.7", "Programming Language :: Python :: 3 :: Only", "Topic :: Scientific/Engineering :: Physics" ], "description": "PennyLane\n#########\n\n.. |CI| image:: https://img.shields.io/travis/com/XanaduAI/pennylane/master.svg?style=popout-square\n :alt: Travis\n :target: https://travis-ci.com/XanaduAI/pennylane/\n\n.. |COV| image:: https://img.shields.io/codecov/c/github/xanaduai/pennylane/master.svg?style=popout-square\n :alt: Codecov coverage\n :target: https://codecov.io/gh/XanaduAI/pennylane\n\n.. |PEP| image:: https://img.shields.io/codacy/grade/83940d926ef5444798a46378e528249d.svg?style=popout-square\n :alt: Codacy grade\n :target: https://app.codacy.com/app/XanaduAI/pennylane?utm_source=github.com&utm_medium=referral&utm_content=XanaduAI/pennylane&utm_campaign=badger\n\n.. |DOC| image:: https://img.shields.io/readthedocs/pennylane.svg?style=popout-square\n :alt: Read the Docs\n :target: https://pennylane.readthedocs.io\n\n.. |VERS| image:: https://img.shields.io/pypi/v/PennyLane.svg?style=popout-square\n :alt: PyPI\n :target: https://pypi.org/project/PennyLane\n\n.. |PY| image:: https://img.shields.io/pypi/pyversions/PennyLane.svg?style=popout-square\n :alt: PyPI - Python Version\n :target: https://pypi.org/project/PennyLane\n\n.. |FORUM| image:: https://img.shields.io/discourse/https/discuss.pennylane.ai/posts.svg?style=popout-square\n :alt: Discourse posts\n :target: https://discuss.pennylane.ai\n\n.. |LIC| image:: https://img.shields.io/pypi/l/PennyLane.svg?style=popout-square\n :alt: PyPI - License\n :target: https://www.apache.org/licenses/LICENSE-2.0\n\n|CI| |COV| |PEP| |DOC| |VERS| |PY| |FORUM|\n\n`PennyLane `_ is a cross-platform Python library for quantum machine learning,\nautomatic differentiation, and optimization of hybrid quantum-classical computations.\n\nFeatures\n========\n\n- **Follow the gradient**. Built-in **automatic differentiation** of quantum circuits\n\n- **Best of both worlds**. Support for **hybrid quantum & classical** models\n\n- **Batteries included**. Provides **optimization and machine learning** tools\n\n- **Device independent**. The same quantum circuit model can be **run on different backends**\n\n- **Large plugin ecosystem**. Install plugins to run your computational circuits on more\n devices, including Strawberry Fields, Rigetti Forest, ProjectQ, and Qiskit\n\n- **Compatible with existing machine learning libraries**. Quantum circuits can interface\n with PyTorch, Tensorflow, or NumPy (via autograd), allowing hybrid CPU-GPU-QPU computations.\n\nAvailable plugins\n=================\n\n* `PennyLane-SF `_: Supports integration with\n `Strawberry Fields `__, a full-stack\n Python library for simulating continuous variable (CV) quantum optical circuits.\n\n\n* `PennyLane-Forest `_: Supports integration\n with `PyQuil `__, the\n `Rigetti Forest SDK `__, and the\n `Rigetti QCS `__, an open-source quantum computation\n framework by Rigetti. Provides device support for the the Quantum Virtual Machine\n (QVM) and Quantum Processing Units (QPUs) hardware devices.\n\n\n* `PennyLane-qiskit `_: Supports\n integration with `Qiskit Terra `__, an open-source quantum\n computation framework by IBM. Provides device support for the Qiskit Aer quantum\n simulators, and IBM QX hardware devices.\n\n\n* `PennyLane-PQ `_: Supports integration with\n `ProjectQ `__, an open-source quantum\n computation framework that supports the IBM quantum experience.\n\n\n* `PennyLane-Qsharp `_: Supports integration\n with the `Microsoft Quantum Development Kit `__,\n a quantum computation framework that uses the Q# quantum programming language.\n\n\nInstallation\n============\n\nPennyLane requires Python version 3.5 and above. Installation of PennyLane, as well\nas all dependencies, can be done using pip:\n\n.. code-block:: bash\n\n $ python -m pip install pennylane\n\n\nGetting started\n===============\n\nFor getting started with PennyLane, check out some of the\n`key concepts `_ behind quantum machine\nlearning, before moving on to some `introductory tutorials `_.\n\nThen, take a deeper dive into quantum machine learning by\nexploring cutting-edge algorithms using PennyLane and near-term quantum hardware,\nwith our collection of\n`QML tutorials `_.\n\nYou can also check out our `documentation `_ for\nmore details on the quantum operations, and to explore the available optimization\ntools provided by PennyLane, and detailed guides on\n`how to write your own `_\nPennyLane-compatible quantum device.\n\nFinally, play around with the numerous `devices and plugins `_\navailable for running your hybrid optimizations \u2014 these include\nIBM Q, provided by the PennyLane-Qiskit plugin, as well as the Rigetti Aspen-1 QPU.\n\n\nContributing to PennyLane\n=========================\n\nWe welcome contributions \u2014 simply fork the PennyLane repository, and then make a\n`pull request `_ containing your contribution.\nAll contributers to PennyLane will be listed as authors on the releases. 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