Metadata-Version: 2.0
Name: qmlt
Version: 0.7.1
Summary: Machine learning and optimization of quantum optical circuits
Home-page: http://xanadu.ai
Author: Xanadu Inc.
Author-email: maria@xanadu.ai
License: LICENSE
Description-Content-Type: UNKNOWN
Platform: UNKNOWN
Classifier: Development Status :: 3 - Alpha
Classifier: Environment :: Console
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Natural Language :: English
Classifier: Operating System :: POSIX
Classifier: Operating System :: MacOS :: MacOS X
Classifier: Operating System :: POSIX :: Linux
Classifier: Operating System :: Microsoft :: Windows
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3 :: Only
Classifier: Topic :: Scientific/Engineering :: Physics
Provides: qmlt
Requires-Dist: scikit-learn (>=0.19)
Requires-Dist: matplotlib (>=2.0)
Requires-Dist: strawberryfields (>=0.7.3)

Quantum Machine Learning Toolbox (QMLT)
###########################################

.. image:: https://img.shields.io/travis/XanaduAI/QMLT/master.svg?style=for-the-badge
    :alt: Travis
    :target: https://travis-ci.org/XanaduAI/QMLT

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    :alt: Codecov coverage
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    :alt: Codacy grade
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.. image:: https://img.shields.io/readthedocs/qmlt.svg?style=for-the-badge
    :alt: Read the Docs
    :target: https://qmlt.readthedocs.io

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    :alt: PyPI - Python Version
    :target: https://pypi.org/project/QMLT


The Quantum Machine Learning Toolbox (QMLT) is a `Strawberry Fields <https://github.com/XanaduAI/strawberryfields>`_ 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 <https://qmlt.readthedocs.io>`_.


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 <https://arxiv.org/abs/1804.03159>`_ 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 <https://u.strawberryfields.ai/slack>`_ -
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.


