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Consolidate and contribute back changes from QHack 2022 - #3
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cnktysz
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Mar 15, 2022
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Hi Amir and others,
Thank you for the suggested changes. I think they look good and definitely a good step forward making QGNNs possible. I was thinking if we should make these changes as a separate file but now I think it is not necessary and the commit name should suggest future users the changes.
Once again thank your the great work!
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Hi Cenk,
First off -- thank you for supporting us during QHack 2022.
I hope this contribution back to QTrkX will be a return on your investment. You're welcome to take all, some, or none of the changes depending on what you think they would benefit the project.
For reference, our final QHack submission is here, which includes our report.
This PR contains the following changes:
sigmoidactivations withrelufollowed by rescaling layers ([0-1] range), which removes the immediate vanishing gradients that show up in the modelFor the final point, please note that only
qc10_pqchas been modified to work with the utility function. For any others you'd like to try, you will need to:symbol_offsetoptional parametersymbolsfrom the PQC from the functionSee
qc10_pqc_identityfor an example.Also, we have a notebook that is an adaptation of the TensorFlow tutorial mentioned above that works to analyze the gradient variance in various ansatze (it loads this info from the
circuits_metadata.json. We found this useful to quickly analyze the existing PQCs outside of the training pipeline. This notebook is not included in this PR, but you can find a copy here.An example graph:
