-
Notifications
You must be signed in to change notification settings - Fork 188
New issue
Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.
By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.
Already on GitHub? Sign in to your account
Add a how-to for catalyst-compiling "Symmetry-invariant quantum machine learning force fields" #1222
base: master
Are you sure you want to change the base?
Conversation
…ne learning force fields" The how-to contains the full code listing, and some primitive tutorial words.
👋 Hey, looks like you've updated some demos! 🐘 Don't forget to update the Please hide this comment once the field(s) are updated. Thanks! |
We should reach out to the original author before deciding whether this should be an additional section to the original demo or a separate new demo. Until then I won't add too many documentation/metadata.json boilerplate, just because it might not be necessary. @josh146 |
There was a problem hiding this comment.
Choose a reason for hiding this comment
The reason will be displayed to describe this comment to others. Learn more.
Thanks @paul0403! It would be worth running black
on the file to fix up some formatting, but otherwise looks good to me! 🎉
@paul0403 It looks like a meta data file is needed so we can look at the built version :) |
8d5569a
to
c72a374
Compare
The how-to contains the full code listing, and some primitive tutorial words.
Before submitting
Please complete the following checklist when submitting a PR:
Ensure that your tutorial executes correctly, and conforms to the
guidelines specified in the README.
Remember to do a grammar check of the content you include.
All tutorials conform to
PEP8 standards.
To auto format files, simply
pip install black
, and thenrun
black -l 100 path/to/file.py
.When all the above are checked, delete everything above the dashed
line and fill in the pull request template.
Title: How to Quantum Just-In-Time Compile "Symmetry-invariant quantum machine learning force fields" with Catalyst
Summary:
As part of Catalyst's work on identifying 10 demos to compile with catalyst, we convert "Symmetry-invariant quantum machine learning force fields", a machine learning workflow that calls a training step function repeatedly and thus can have significant performance boosts with catalyst compilation.
Note: the original demo is from an external user, so we should NOT merge this until we have reached out to them. The epic requirement is considered fulfilled just by the opening of this PR.
Relevant references:
Possible Drawbacks:
Related GitHub Issues:
[sc-72938]
If you are writing a demonstration, please answer these questions to facilitate the marketing process.
GOALS — Why are we working on this now?
Promote Catalyst by demonstrating the performance improvements it offers by QJIT compiling a relatively complex end-to-end quantum workflow.
AUDIENCE — Who is this for?
Chemistry and quantum machine learning researchers.
KEYWORDS — What words should be included in the marketing post?
Which of the following types of documentation is most similar to your file?
(more details here)