qmlt 0.7.1

Features

The Quantum Machine Learning Toolbox supports:

• The training of user-provided variational circuits

• Automatic and numerical differentiation methods to compute gradients of circuit outputs

• Optimization, supervised and unsupervised learning tasks

• Regularization of circuit parameters

• Logging of training results

• Monitoring and visualization of training through matplotlib and TensorBoard

• Saving and restoring trained models

• Parallel computation/GPU usage for TensorFlow-based models

To get started, please see the online documentation.

Installation

Installation of SFOpenBoson, as well as all required Python packages mentioned above, can be done using pip:

$ python -m pip install qmlt

Code authors

Maria Schuld and Josh Izaac.

If you are doing research using Strawberry Fields, please cite our whitepaper and the QMLT documentation:

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

Maria Schuld and Josh Izaac. Xanadu Quantum Machine Learning Toolbox documentation. https://qmlt.readthedocs.io.

Support

• Source Code: https://github.com/XanaduAI/QMLT

• Issue Tracker: https://github.com/XanaduAI/QMLT/issues

If you are having issues, please let us know by posting the issue on our Github issue tracker.

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

License

QMLT is free and open source, released under the Apache License, Version 2.0.