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.
- Provides two devices to be used with PennyLane:
strawberryfields.fockandstrawberryfields.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 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
PennyLane-SF requires the following libraries be installed:
- Python >=3.6
as well as the following Python packages:
- PennyLane >=0.7
- StrawberryFields >=0.11.2
If you currently do not have Python 3 installed, we recommend Anaconda for Python 3, a distributed version of Python packaged for scientific computation.
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
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.
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.
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
- Source Code: https://github.com/PennyLaneAI/pennylane-sf
- Issue Tracker: https://github.com/PennyLaneAI/pennylane-sf/issues
- PennyLane Forum: https://discuss.pennylane.ai
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.
PennyLane-SF is free and open source, released under the Apache License, Version 2.0.