{ "info": { "author": "Xanadu Inc.", "author_email": "maria@xanadu.ai", "bugtrack_url": null, "classifiers": [ "Development Status :: 3 - Alpha", "Environment :: Console", "Intended Audience :: Science/Research", "License :: OSI Approved :: Apache Software License", "Natural Language :: English", "Operating System :: MacOS :: MacOS X", "Operating System :: Microsoft :: Windows", "Operating System :: POSIX", "Operating System :: POSIX :: Linux", "Programming Language :: Python", "Programming Language :: Python :: 3", "Programming Language :: Python :: 3.5", "Programming Language :: Python :: 3.6", "Programming Language :: Python :: 3 :: Only", "Topic :: Scientific/Engineering :: Physics" ], "description": "Quantum Machine Learning Toolbox (QMLT)\n###########################################\n\n.. image:: https://img.shields.io/travis/XanaduAI/QMLT/master.svg?style=for-the-badge\n :alt: Travis\n :target: https://travis-ci.org/XanaduAI/QMLT\n\n.. image:: https://img.shields.io/codecov/c/github/xanaduai/qmlt/master.svg?style=for-the-badge\n :alt: Codecov coverage\n :target: https://codecov.io/gh/XanaduAI/QMLT\n\n.. image:: https://img.shields.io/codacy/grade/acc9267c77f14a84ae8105732429a799.svg?style=for-the-badge\n :alt: Codacy grade\n :target: https://app.codacy.com/app/XanaduAI/QMLT?utm_source=github.com&utm_medium=referral&utm_content=XanaduAI/QMLT&utm_campaign=badger\n\n.. image:: https://img.shields.io/readthedocs/qmlt.svg?style=for-the-badge\n :alt: Read the Docs\n :target: https://qmlt.readthedocs.io\n\n.. image:: https://img.shields.io/pypi/pyversions/QMLT.svg?style=for-the-badge\n :alt: PyPI - Python Version\n :target: https://pypi.org/project/QMLT\n\n\nThe 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.\n\n\nFeatures\n========\n\n\nThe Quantum Machine Learning Toolbox supports:\n\n* The training of user-provided variational circuits\n\n* Automatic and numerical differentiation methods to compute gradients of circuit outputs\n\n* Optimization, supervised and unsupervised learning tasks\n\n* Regularization of circuit parameters\n\n* Logging of training results\n\n* Monitoring and visualization of training through matplotlib and TensorBoard\n\n* Saving and restoring trained models\n\n* Parallel computation/GPU usage for TensorFlow-based models\n\nTo get started, please see the online `documentation `_.\n\n\nInstallation\n============\n\nInstallation of SFOpenBoson, as well as all required Python packages mentioned above, can be done using pip:\n::\n\n $ python -m pip install qmlt\n\n\nCode authors\n============\n\nMaria Schuld and Josh Izaac.\n\nIf you are doing research using Strawberry Fields, please cite `our whitepaper `_ and the QMLT documentation:\n\n Nathan Killoran, Josh Izaac, Nicol\u00e1s Quesada, Ville Bergholm, Matthew Amy, and Christian Weedbrook. Strawberry Fields: A Software Platform for Photonic Quantum Computing. *arXiv*, 2018. arXiv:1804.03159\n\n Maria Schuld and Josh Izaac. Xanadu Quantum Machine Learning Toolbox documentation. https://qmlt.readthedocs.io.\n\n\nSupport\n=======\n\n- **Source Code:** https://github.com/XanaduAI/QMLT\n- **Issue Tracker:** https://github.com/XanaduAI/QMLT/issues\n\nIf you are having issues, please let us know by posting the issue on our Github issue tracker.\n\nWe also have a `Strawberry Fields Slack channel `_ -\ncome join the discussion and chat with our Strawberry Fields team.\n\n\nLicense\n=======\n\nQMLT is **free** and **open source**, released under the Apache License, Version 2.0.\n\n\n", "description_content_type": "", "docs_url": null, "download_url": "", "downloads": { "last_day": -1, "last_month": -1, "last_week": -1 }, "home_page": "http://xanadu.ai", "keywords": "", "license": "LICENSE", "maintainer": "", "maintainer_email": "", "name": "qmlt", "package_url": "https://pypi.org/project/qmlt/", "platform": "", "project_url": "https://pypi.org/project/qmlt/", "project_urls": { "Homepage": "http://xanadu.ai" }, "release_url": "https://pypi.org/project/qmlt/0.7.1/", "requires_dist": [ "scikit-learn (>=0.19)", "matplotlib (>=2.0)", "strawberryfields (>=0.7.3)" ], "requires_python": "", "summary": "Machine learning and optimization of quantum optical circuits", "version": "0.7.1" }, "last_serial": 4027881, "releases": { "0.7.0": [], "0.7.1": [ { "comment_text": "", "digests": { "md5": "f9b3f7755c625481b122b53430a971ce", "sha256": "d939f8c82bb72570bbbd2822eaaf414473ed77de1860a7a8168dea43ed5eb63b" }, "downloads": -1, "filename": "qmlt-0.7.1-py3-none-any.whl", "has_sig": false, "md5_digest": "f9b3f7755c625481b122b53430a971ce", "packagetype": "bdist_wheel", "python_version": "py3", "requires_python": null, "size": 34913, "upload_time": "2018-07-03T20:47:00", "url": "https://files.pythonhosted.org/packages/d1/c6/942d0468cee069c99f6792543e6b8bacaa5aa8e3699acecc51730e497581/qmlt-0.7.1-py3-none-any.whl" }, { "comment_text": "", "digests": { "md5": "715d7249d3f65141ad879bf1a5549ad7", "sha256": "5e0d69ef70380fe4ae9a7ee63842f89ae1913533b2b1bd0be32853f3fe4df9b6" }, "downloads": -1, "filename": "qmlt-0.7.1.tar.gz", "has_sig": false, "md5_digest": "715d7249d3f65141ad879bf1a5549ad7", "packagetype": "sdist", "python_version": "source", "requires_python": null, "size": 43907, "upload_time": "2018-07-03T20:47:01", "url": "https://files.pythonhosted.org/packages/5f/64/96cb71d2bd1638d8906990316f60edba86057e7266e7cb265c6d4a13bb96/qmlt-0.7.1.tar.gz" } ] }, "urls": [ { "comment_text": "", "digests": { "md5": "f9b3f7755c625481b122b53430a971ce", "sha256": "d939f8c82bb72570bbbd2822eaaf414473ed77de1860a7a8168dea43ed5eb63b" }, "downloads": -1, "filename": "qmlt-0.7.1-py3-none-any.whl", "has_sig": false, "md5_digest": "f9b3f7755c625481b122b53430a971ce", "packagetype": "bdist_wheel", "python_version": "py3", "requires_python": null, "size": 34913, "upload_time": "2018-07-03T20:47:00", "url": "https://files.pythonhosted.org/packages/d1/c6/942d0468cee069c99f6792543e6b8bacaa5aa8e3699acecc51730e497581/qmlt-0.7.1-py3-none-any.whl" }, { "comment_text": "", "digests": { "md5": "715d7249d3f65141ad879bf1a5549ad7", "sha256": "5e0d69ef70380fe4ae9a7ee63842f89ae1913533b2b1bd0be32853f3fe4df9b6" }, "downloads": -1, "filename": "qmlt-0.7.1.tar.gz", "has_sig": false, "md5_digest": "715d7249d3f65141ad879bf1a5549ad7", "packagetype": "sdist", "python_version": "source", "requires_python": null, "size": 43907, "upload_time": "2018-07-03T20:47:01", "url": "https://files.pythonhosted.org/packages/5f/64/96cb71d2bd1638d8906990316f60edba86057e7266e7cb265c6d4a13bb96/qmlt-0.7.1.tar.gz" } ] }