Description:

qmlt 0.7.1

The Quantum Machine Learning Toolbox (QMLT) is a Strawberry Fields application that simplifies the optimization of variational quantum circuits. Tasks for the QMLT range from variational eigensolvers and unitary learning to supervised and unsupervised machine learning with models based on a variational circuit.

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.

License

For personal and professional use. You cannot resell or redistribute these repositories in their original state.

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