Add quantum kernel pre-screening demo based on Huang et al. #1569
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Hi @daniela-angulo , I checked the preview and some things did not render properly, do I need to do anything? |
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Hi @andynader, yeah, I saw that. |
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Hi @andynader, I think I addressed all the formatting issues. Could you verify that for me? Is the demo looking the way it is intended to? |
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Hi Daniela, thank you for your hard work. I changed the code as you recommended; should we also make some figures such as the scatter plot and embedding circuit plots smaller or do you think they're good? |
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Yes, I think we could probably do that. |
Hi Daniela, yes he told me you have an in house graphic designer who can make them, how should I move forward? Should I contact Ben? |
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I will also work on making the figures smaller this weekend and push the changes |
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Hi! |
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Such a cool demo, the main idea is clearly well presented.
I only added a few comments. Thanks!
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| r"""Is a Quantum Kernel Worth Exploring? A ‘:math:`g`-first’ Reality Check | |||
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is this your preferred title or the on in the metadata file? they are different
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Updated to match the metadata, haven't committed yet though.
| From a practitioner’s perspective, such a **pre-screening test** is invaluable: it lets us rule out | ||
| quantum kernels that don’t offer any potential quantum advantage right from the start. | ||
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| Huang *et al.* (https://arxiv.org/abs/2011.01938) introduced exactly this test. Their proposed |
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we should have a reference section at the end, before the appendix. It doesn't matter if all we cite is the same article several times.
You can take a look at other demos in the open PRs in the repo #1577
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Is the best way to incorporate this directly into the demo file, put the reference here, or in the notebook and then convert again?
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best to do it in the demo file directly. If you run into difficulties, let me know, and I'll help.
| # :math:`K_{\text{RBF}}` obtained from: | ||
| # | ||
| # .. math:: k_{\text{RBF}}(x, x') = \exp(-\gamma \|x - x'\|^2) | ||
| # | ||
| # :math:`K_{\text{QK-E1}}` obtained from: | ||
| # | ||
| # .. math:: k_{\text{QK-E1}}(x, x') = |\langle \psi_{\text{E1}}(x) \mid \psi_{\text{E1}}(x') \rangle|^2 | ||
| # | ||
| # :math:`K_{\text{QK-E2}}` obtained from: | ||
| # | ||
| # .. math:: k_{\text{QK-E2}}(x, x') = |\langle \psi_{\text{E2}}(x) \mid \psi_{\text{E2}}(x') \rangle|^2 | ||
| # | ||
| # :math:`K_{\text{PQK-E1}}` obtained from: | ||
| # | ||
| # .. math:: k_{\text{PQK-E1}}(x, x') = \exp\left( -\gamma \|v_{\text{E1}}(x) - v_{\text{E1}}(x')\|^2 \right) | ||
| # | ||
| # :math:`K_{\text{PQK-E2}}` obtained from: | ||
| # | ||
| # .. math:: k_{\text{PQK-E2}}(x, x') = \exp\left( -\gamma \|v_{\text{E2}}(x) - v_{\text{E2}}(x')\|^2 \right) |
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I think we might leave this out since it is repeated from the above equations. Just to shorten up the demo a bit.
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No problem. Should I update myself?
| # - The role of :math:`g` is *not* to predict which kernel will perform best on a given task, but | ||
| # rather to obtain a collection of kernels that have the *potential* to offer an advantage. | ||
| # | ||
| # Here, PQK-E1 and PQK-E2 both had the potential for an advantage over classical, but PQK-E2 is the |
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over classical kernels or classical ...?
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Over classical kernels, but the authors found that this holds true for other algorithms too. Should I add this?
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yes, it is possible.
Co-authored-by: Daniela Angulo <[email protected]>
Co-authored-by: Daniela Angulo <[email protected]>
Co-authored-by: Daniela Angulo <[email protected]>
Co-authored-by: Daniela Angulo <[email protected]>
Co-authored-by: Daniela Angulo <[email protected]>
Co-authored-by: Daniela Angulo <[email protected]>
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Thanks for your hard work @daniela-angulo, I committed the suggestions, hope this didn't break anything. What's left is to make the figures smaller, and include the changes in my replies to your comments. Can I pull the latest changes and update myself? |
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Thanks for reviewing this, @andynader . Yes, you can update the changes yourself and commit them to the repo. |
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Hi, I wanted to let you know I will be away for the next three weeks. I will continue working on the demo once I am back. Thank you! |
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Absolutely, thank you for your hard work Daniela! |
2 participants
Title:
Before You Train: Pre-screening Quantum Kernels with Geometric Difference
Summary:
This demo implements the geometric difference metric (g) from Huang et al. (2021) for pre-screening quantum kernels before training. The metric quantifies how differently a quantum kernel's geometry represents data compared to a classical kernel, allowing practitioners to identify promising quantum approaches early and avoid wasting time on kernels with no potential advantage. Using synthetic two-moons data, we demonstrate how to calculate g for multiple quantum kernel variants (fidelity-based and projected) and interpret the results to guide kernel selection.
Relevant references:
Possible Drawbacks:
None
Related GitHub Issues:
None
GOALS — Why are we working on this now?
Provide practitioners with a practical tool for quantum kernel evaluation. Address a common pain point where researchers spend significant time tuning quantum kernels that fundamentally cannot outperform classical ones.
AUDIENCE — Who is this for?
QML researchers and practitioners working with quantum kernels, ML engineers exploring quantum advantages, and anyone interested in practical quantum machine learning workflows.
KEYWORDS:
quantum kernels, geometric difference, pre-screening, kernel methods, SVM, quantum machine learning, quantum advantage
Which type of documentation?