Skip to content

PennyLaneAI/pennylane-sf

master
Switch branches/tags
Code

Latest commit

 

Git stats

Files

Permalink
Failed to load latest commit information.
Type
Name
Latest commit message
Commit time
 
 
doc
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

PennyLane Strawberry Fields Plugin

GitHub Workflow Status (branch) Codecov coverage CodeFactor Grade Read the Docs PyPI PyPI - Python Version

The PennyLane-SF plugin integrates the StrawberryFields photonic quantum computing framework with PennyLane's quantum machine learning capabilities.

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

Strawberry Fields is a full-stack Python library for designing, simulating, and optimizing photonic quantum circuits.

The plugin documentation can be found here: PennyLane-Strawberry Fields.

Features

  • Provides two devices to be used with PennyLane: strawberryfields.fock and strawberryfields.gaussian. These provide access to the Strawberry Fields Fock and Gaussian backends respectively.
  • Supports all core PennyLane operations and observables across the two devices.
  • Combine Strawberry Fields optimized simulator suite with PennyLane's automatic differentiation and optimization.

Installation

Installation of PennyLane-SF, as well as all required Python packages, can be installed via pip:

$ python -m pip install pennylane-sf

Make sure you are using the Python 3 version of pip.

Alternatively, you can install PennyLane-SF from the source code by navigating to the top directory and running

$ python setup.py install

Dependencies

PennyLane-SF requires the following libraries be installed:

as well as the following Python packages:

If you currently do not have Python 3 installed, we recommend Anaconda for Python 3, a distributed version of Python packaged for scientific computation.

Software tests

To ensure that PennyLane-SF is working correctly after installation, the test suite can be run by navigating to the source code folder and running

$ make test

Documentation

To build the HTML documentation, go to the top-level directory and run

$ make docs

The documentation can then be found in the doc/_build/html/ directory.

Contributing

We welcome contributions - simply fork the PennyLane-SF repository, and then make a pull request containing your contribution. All contributers to PennyLane-SF will be listed as authors on the releases.

We also encourage bug reports, suggestions for new features and enhancements, and even links to cool projects or applications built on PennyLane and Strawberry Fields.

Authors

Josh Izaac, Ville Bergholm, Maria Schuld, Nathan Killoran and Christian Gogolin

If you are doing research using PennyLane and StrawberryFields, please cite our papers:

Ville Bergholm, Josh Izaac, Maria Schuld, Christian Gogolin, and Nathan Killoran. PennyLane: Automatic differentiation of hybrid quantum-classical computations. 2018. arXiv:1811.04968

Nathan Killoran, Josh Izaac, Nicolás Quesada, Ville Bergholm, Matthew Amy, and Christian Weedbrook. Strawberry Fields: A Software Platform for Photonic Quantum Computing. 2018. arXiv:1804.03159

Support

If you are having issues, please let us know by posting the issue on our Github issue tracker, or by asking a question in the forum.

We also have a Strawberry Fields Slack channel - come join the discussion and chat with our Strawberry Fields team.

License

PennyLane-SF is free and open source, released under the Apache License, Version 2.0.

About

The PennyLane-SF plugin integrates Xanadu's Strawberry Fields photonic quantum simulators and hardware with PennyLane's quantum machine learning capabilities.

Resources

License

Code of conduct

Stars

Watchers

Forks

Packages

No packages published