PennyLane is a cross-platform Python library for differentiable quantum programming. Train a quantum computer the same way as a neural network.
You can use PennyLane for quantum computing, quantum machine learning, quantum chemistry, and hybrid quantum-classical computations. Extensive examples, tutorials, and demos are available at https://pennylane.ai/qml.
- Device independent. The same quantum circuit model can be run on different backends. Install plugins to access even more devices, including Strawberry Fields, IBM Q, Google Cirq, Rigetti Forest, and Microsoft QDK.
- Best of both worlds. Support for hybrid quantum and classical models; connect quantum hardware with PyTorch, TensorFlow, and NumPy.
- Follow the gradient. Hardware-friendly automatic differentiation of quantum circuits.
- Batteries included. Built-in tools for optimization, machine learning, and quantum chemistry.
- PennyLane-SF: Supports integration with Strawberry Fields, a full-stack Python library for simulating continuous variable (CV) quantum optical circuits.
- 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.
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 pennylaneFor an introduction to quantum machine learning, we have several guides and resources available on our QML website:
Then, take a deeper dive into quantum machine learning by exploring cutting-edge algorithms using PennyLane and near-term quantum hardware, with our collection of QML demonstrations.
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.
Finally, play around with the numerous devices and plugins available for running your hybrid optimizations — these include IBM Q, provided by the PennyLane-Qiskit plugin, as well as the Rigetti Aspen QPU.
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.
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
- Source Code: https://github.com/XanaduAI/pennylane
- Issue Tracker: https://github.com/XanaduAI/pennylane/issues
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.
PennyLane is free and open source, released under the Apache License, Version 2.0.