https://github.com/XanaduAI/QMLT

The Quantum Machine Learning Toolbox (QMLT) is a Strawberry Fields application that simplifies the optimization of variational quantum circuits (also known as parametrized quantum circuits).
https://github.com/XanaduAI/QMLT

deep-learning machine-learning neural-network optimization quantum quantum-computing tensorflow

Last synced: 2 months ago
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The Quantum Machine Learning Toolbox (QMLT) is a Strawberry Fields application that simplifies the optimization of variational quantum circuits (also known as parametrized quantum circuits).

• Host: GitHub
• URL: https://github.com/XanaduAI/QMLT
• Owner: XanaduAI
• License: apache-2.0
• Archived: true
• Created: 2018-07-03T19:30:29.000Z (over 6 years ago)
• Default Branch: master
• Last Pushed: 2020-03-03T04:59:16.000Z (almost 5 years ago)
• Last Synced: 2024-09-28T10:20:57.431Z (4 months ago)
• Topics: deep-learning, machine-learning, neural-network, optimization, quantum, quantum-computing, tensorflow
• Language: Python
• Homepage: https://qmlt.readthedocs.io
• Size: 3.69 MB
• Stars: 114
• Watchers: 11
• Forks: 23
• Open Issues: 1
• Metadata Files:
  • Readme: README.rst
  • License: LICENSE

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README

        
Notice: This library is no longer actively maintained. Its spiritual successor is `PennyLane `_

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Quantum Machine Learning Toolbox (QMLT)

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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 the QMLT, as well as all required Python packages mentioned above, can be done using pip:

::

    $ python -m pip install qmlt

Tests

=====

To run all tests please run the following line from the main directory:

::

    $ python -m unittest discover tests

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.