This repository contains introductory materials on Quantum Machine Learning, as well as Python code demos. Both can be accessed at PennyLane's quantum machine learning page. The content consists of four main areas:
What is quantum machine learning?. Understand what quantum computing means for machine learning.
QML tutorials and demos. Take a dive into quantum machine learning with fully-coded implementations of major works.
Key concepts of QML. A glossary of key ideas for quantum machine learning and optimization.
QML videos. A selection of curated expert videos discussing various aspects of quantum machine learning.
You can contribute by submitting a demo implementing a recent quantum machine learning paper/result.
Demos are written in the form of an executable Python script.
Any package listed in requirements.txt you can assume is available to be imported.
Matplotlib plots will be automatically rendered and displayed on the QML website.
Note: try and keep execution time of your script to within 10 minutes.
If you would like to write the demo using a Jupyter notebook, you can convert
the notebook to the required executable Python format by using this script.
All demos should have a file name beginning with tutorial_.
The python files are saved in the demonstrations directory.
Restructured Text
sections may be anywhere within the script by beginning the comment with
79 hashes (#). These are useful for breaking up large code-blocks.
You may add figures within ReST comments by using the following syntax:
##############################################################################
#.. figure:: ../demonstrations/<demo name>/image.png
# :align: center
# :width: 90%where <demo name> is a sub-directory with the name of
your demo.
When complete, create a gallery link to your demo, by adding the
following to demonstrations.rst:
.. customgalleryitem::
:tooltip: An extended description of the demo
:figure: demonstrations/<demo name>/thumbnail.png
:description: :doc:`demos/pytorch_noise`You should also add a link to your demo to the table of contents, by adding to the
end of the .. toctree::.
Finally, run your script through the Black Python formatter,
pip install black
black -l 100 demo_new.pyand you are ready to submit a pull request!
While you are free to be as creative as you like with your demo, there are a couple of guidelines to keep in mind.
Submissions should include your name (and optionally email) at the top under the title.
All contributions must be made under the Apache 2.0 license.
The title should be clear and concise, and if based on a paper it should be similar to the paper that is being implemented.
All demos should include a summary below the title. The summary should be 1-3 sentences that makes clear the goal and outcome of the demo, and links to any papers/resources used.
Code should be clearly commented and explained, either as a ReST-formatted comment or a standard Python comment.
Thumbnails should be legible, interesting, and unique --- but not too busy! Any included text should be minimal and legible
If your content contains random variables/outputs, a fixed seed should be set for reproducibility.
All content must be original or free to reuse subject to license compatibility. For example, if you are implementing someone elses research, reach out first to recieve permission to reproduce exact figures. Otherwise, avoid direct screenshots from papers, and instead refer to figures in the paper within the text.
All submissions must pass code review before being merged into the repository.
To build the website locally, simply run make html. The rendered HTML files
will now be available in _build/html. Open _build/html/index.html to browse
the built site locally.
Note that the above command may take some time, as all demos will be executed and built! Once built, only modified demos will be re-executed/re-built.
Alternatively, you may run make html-norun to build the website without executing
demos.
If you are having issues, please let us know by posting the issue on our GitHub issue tracker.
We are committed to providing a friendly, safe, and welcoming environment for all. Please read and respect the Code of Conduct.
The materials and demos in this repository are free and open source, released under the Apache License, Version 2.0.
The file custom_directives.py is available under the BSD 3-Clause License with
Copyright (c) 2017, Pytorch contributors.