-
Notifications
You must be signed in to change notification settings - Fork 349
Dataset for Benchmarking Variational Fast Forward Pipielines based on Hydrogen Molecule Simulation #916
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
base: main
Are you sure you want to change the base?
Conversation
There seem to be lint and mypy problems I missed to address. I'll fix and re-commit. Meanwhile, any thoughts on making unittests faster and making the transpilation step qiskit 2.0.0 friendly would be very helpful! |
The CI issues have been fixed so I updated the branch (via the update button that was here) so any issues now would be just down to this PR - unless it has a random failure for which there is an issue #903 around that, |
The failure in MacOS 3.12 in this |
Just merged the latest updates in this branch |
I'm not able to fix that docstring :( |
If I look at the Checks log here the style check is also complaining i.e the black check that says it would reformat the files,as below. This is done from the makefile using
|
Summary
Benchmarking dataset for variational fast-forwarding (VFF) pipelines using the time evolution of quantum states from hydrogen-based molecules. The dataset is structured to support learning-based extrapolation of quantum dynamics: short-term, potentially noisy training states are provided alongside long-term, exact reference evolutions.
The data generator gives us the following:
⎢ѱHK〉
: The Hartree Fock State of the systemX_train
:[Δt, 2Δtm …NΔt]
which are uniform timestamps at which short-term evolutions are recordedY_train
:U(Δt)⎢ѱHK〉...U(NΔt)⎢ѱHK〉
which are the short-term evolutions but with noiseX_test
:[PΔt…QΔt]
which are the long-term timestampsY_test
:U(PΔt)⎢ѱHK〉…U(QΔt)⎢ѱHK〉
which are the long-term noiseless evolutionsDetails and comments
Literature that uses a similar training procedure for VFFs: https://quantum-journal.org/papers/q-2024-03-13-1278/pdf/