PennyLane Documentation

Release:0.7.0

PennyLane is a cross-platform Python library for quantum machine learning, automatic differentiation, and optimization of hybrid quantum-classical computations.

Features

_images/intro.png
  • Follow the gradient. Built-in automatic differentiation of quantum circuits.
  • Best of both worlds. Support for hybrid quantum and classical models; connect quantum hardware with PyTorch, TensorFlow, and NumPy.
  • Batteries included. Provides optimization and machine learning tools.
  • 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, Microsoft QDK, and ProjectQ.

Getting started

For getting started with PennyLane, check out some of the key concepts behind quantum machine learning, before moving on to some introductory tutorials.

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 tutorials.

You can also check out the Using PennyLane section for more details on the quantum operations, and to explore the available optimization tools provided by PennyLane. We also have a detailed guide 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-1 QPU provided by PennyLane-Forest.

How to cite

If you are doing research using PennyLane, please cite

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

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.