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arXiv Machine Learning By Seokjun Lee, Sergio A. Ortega, Daniel K. Park, Namshik Han

VQIL: Visible Quantum Interference Learning for Perturbation Response Ranking on Graphs

Read the original on arXiv Machine Learning →

The paper introduces VQIL, a trainable Szegedy quantum walk that learns how local perturbations propagate through a graph. By optimizing edge weights, link phases, and node phase rotations via backpropagation, VQIL produces node rankings based on a geometrically discounted coherent sum of quantum-walk amplitudes. Experiments on AC power‑flow and gene perturbation datasets show significant improvements in Spearman rank correlation compared to untrained walks and classical baselines, demonstrating the method’s scalability and interpretability.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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