Add quantum kernel pre-screening demo based on Huang et al. #1569
Conversation
this is for validation and make a preview
Your preview is ready 🎉!You can view your changes here |
| Hi @daniela-angulo , I checked the preview and some things did not render properly, do I need to do anything? |
| Hi @andynader, yeah, I saw that. |
| 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? |
| print("E1 Embedding Circuit:") | ||
| fig, ax = qml.draw_mpl(embedding_E1)(np.zeros(n_qubits)) | ||
| plt.show() | ||
| | ||
| print("\nE2 Embedding Circuit:") | ||
| fig, ax = qml.draw_mpl(embedding_E2)(np.zeros(n_qubits)) | ||
| plt.show() |
There was a problem hiding this comment.
these lines are not going to plot the figures as intended. It is plotting the figures first and then printing the text (see rendering). Could you add those E1 and E2 as titles for the figures? if that's too annoying, I guess we can start a new code block for the E2 figure haha, as a way to circumvent this, let me know.
| plt.tight_layout() | ||
| plt.suptitle(f"Gram Matrix Visualizations (first {subset_size}×{subset_size} entries)", y=1.02) | ||
| plt.show() |
There was a problem hiding this comment.
these figures are very pretty but one can barely see them, maybe another layout could be better? like a (2,3) array?
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?