PennyLane is a cross-platform Python library for quantum machine learning, automatic differentiation, and optimization of hybrid quantum-classical computations.
- Follow the gradient. Built-in automatic differentiation of quantum circuits
- Best of both worlds. Support for hybrid quantum & classical models
- Batteries included. Provides optimization and machine learning tools
- Device independent. The same quantum circuit model can be run on different backends
- Large plugin ecosystem. Install plugins to run your computational circuits on more devices, including Strawberry Fields, Rigetti Forest, ProjectQ, and Qiskit
- Compatible with existing machine learning libraries. Quantum circuits can interface with PyTorch, Tensorflow, or NumPy (via autograd), allowing hybrid CPU-GPU-QPU computations.
- PennyLane-SF: Supports integration with Strawberry Fields, a full-stack Python library for simulating continuous variable (CV) quantum optical circuits.
- 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-qiskit: Supports integration with Qiskit Terra, an open-source quantum computation framework by IBM. Provides device support for the Qiskit Aer quantum simulators, and IBM QX hardware devices.
- PennyLane-PQ: Supports integration with ProjectQ, an open-source quantum computation framework that supports the IBM quantum experience.
- PennyLane-Qsharp: Supports integration with the Microsoft Quantum Development Kit, a quantum computation framework that uses the Q# quantum programming language.
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
Our documentation is also a great starting point to familiarize yourself with the hybrid classical-quantum machine learning approach, and explore the available optimization tools provided by PennyLane. Play around with the numerous devices and plugins available for running your hybrid optimizations — these include the IBM QX4 quantum chip, provided by the PennyLane-PQ and PennyLane-qiskit plugins, as well as the Rigetti Aspen-1 QPU.
Finally, detailed documentation on the PennyLane API is provided, for full details on available quantum operations and expectations, and detailed guides on how to write your own PennyLane-compatible quantum device.
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.
Don't forget to submit your PennyLane contribution to the Xanadu Quantum Software Competition, with prizes of up CAD$1000 on offer.
See our contributions page for more details.
Ville Bergholm, Josh Izaac, Maria Schuld, Christian Gogolin, and Nathan Killoran.
If you are doing research using PennyLane, please cite our paper:
Ville Bergholm, Josh Izaac, Maria Schuld, Christian Gogolin, Carsten Blank, Keri McKiernan, 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.