Skip to main content

PennyLane is a Python quantum machine learning library by Xanadu Inc.

Project description

PennyLane

Travis Codecov coverage Codacy grade Read the Docs PyPI PyPI - Python Version Discourse posts

PennyLane is a cross-platform Python library for differentiable programming of quantum computers.

PennyLane provides open-source tools for quantum machine learning, quantum computing, quantum chemistry, and hybrid quantum-classical computing. Extensive examples, tutorials, and demos are available at https://pennylane.ai/qml.

Key Features

  • Device independent. Access quantum hardware and simulators from Xanadu Strawberry Fields, IBM Q, Google Cirq, Rigetti Forest, and Microsoft QDK.

  • Best of both worlds. Build hybrid models by connecting quantum hardware to PyTorch, TensorFlow, Keras, and NumPy.

  • Follow the gradient. Hardware-friendly automatic differentiation of quantum circuits.

  • Batteries included. Built-in tools for quantum machine learning, optimization, and quantum chemistry.

Getting started

For an introduction to quantum machine learning, we have several guides and resources available on PennyLane’s quantum machine learning page:

You can also check out our documentation for quickstart guides to using PennyLane, and detailed developer guides on how to write your own PennyLane-compatible quantum device.

Available plugins

  • PennyLane-SF: Supports integration with Strawberry Fields, a full-stack Python library for simulating photonic quantum computing.

  • PennyLane-qiskit: Supports integration with Qiskit, an open-source quantum computation framework by IBM. Provides device support for the Qiskit Aer quantum simulators, and IBM Q hardware devices.

  • PennyLane-cirq: Supports integration with Cirq, an open-source quantum computation framework by Google.

  • PennyLane-Forest: Supports integration with PyQuil, the Rigetti Forest SDK, and the Rigetti 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: Supports integration with the Microsoft 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.

Installation

PennyLane requires Python version 3.5 and above. Installation of PennyLane, as well as all dependencies, can be done using pip:

$ python -m pip install pennylane

Contributing to PennyLane

We welcome contributions — simply fork the PennyLane repository, and then make a pull request 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 for more details.

Authors

PennyLane is the work of many contributors.

If you are doing research using PennyLane, please cite our paper:

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

Support

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 - 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.

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

PennyLane-0.9.0.tar.gz (225.8 kB view hashes)

Uploaded Source

Built Distribution

PennyLane-0.9.0-py3-none-any.whl (239.7 kB view hashes)

Uploaded Python 3

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page