[DEMO] Quantum Re-Uploading Unit (QRU): function fitting with data re-uploading (PennyLane + PyTorch)

[LeaCasse]

General information

Name
Léa Cassé (github: LeaCasse)

Affiliation
University of Waikato / Institut Polytechnique de Paris (PhD)

Twitter
N/A

Image

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Demo information

Title
Quantum Re-Uploading Unit (QRU): function fitting with data re-uploading (PennyLane + PyTorch)

Abstract
This community demo introduces a minimal 1-qubit Quantum Re-Uploading Unit (QRU) implemented in PennyLane with a clean PyTorch training loop. The notebook explains the re-uploading idea (injecting data at every layer), trains the QRU on a simple regression task (sine fitting), and includes learning curves plus basic parameter inspection for reproducibility.

Relevant links

• GitHub repository: https://github.com/LeaCasse/qru-template
• Jupyter notebook (community demo): https://github.com/LeaCasse/qru-template/tree/main/demos/QRU_PennyLane_Community_Demo.ipynb
• (Optional) Related resources/papers: https://scholar.google.com/scholar?oi=bibs&cluster=17309207027991833662&btnI=1&hl=fr https://ieeexplore.ieee.org/abstract/document/11250260/

[CatalinaAlbornoz]

Hi @LeaCasse ,

Thanks for your submission!

At a first glance the demo looks good 👌
We'll assign someone from our team for a more in-depth review.
Would you like to share a LinkedIn handle with us to tag you when we do a marketing post?

[LeaCasse]

Hi @CatalinaAlbornoz,

Thanks for the quick feedback!

I'm glad the demo looks good at first glance.
My LinkedIn is: https://www.linkedin.com/in/l%C3%A9a-cass%C3%A9-51b961215/
Happy to provide any additional information during the review.

[CatalinaAlbornoz]

Hi @LeaCasse,

I wanted to let you know that we've assigned a reviewer to your demo! They will be getting back to you here within a week or two. Thanks again for your patience and for making this demo!

[drdren]

Hi @LeaCasse, I reviewed your demo "QRU_PennyLane_Community_Demo.ipynb" and found it to be clear, purposeful, and suitable to be a PennyLane community demo.

If there is any place for improvement, consider adding language in the "5. Train" section that hints that the codeblock immediately below will take some time to run such as "Note that this code can take a little bit of time to run, so be patient." For reference, that codeblock took 9 minutes to run on Google Colab.

Otherwise, I would be happy to have your contribution as a community demo. Nice work!

cc: @CatalinaAlbornoz

[drdren]

In terms of next steps, @LeaCasse, we will create a card for the demo in our community demo page & market it on social media over the next few weeks. We will update you when we know the dates for those. Thanks!

[LeaCasse]

Hi @drdren, thank you very much for the careful review and for the kind feedback, I appreciate it. I’ve added a note in the “5. Train” section to indicate that the training cell may take several minutes to run depending on the environment, as you suggested.

Thanks as well for the update on the next steps, I’m glad to hear the demo will be included on the community page, and I’ll look forward to the upcoming updates!