VQIL: Visible Quantum Interference Learning for Perturbation Response Ranking on Graphs
VQIL introduces a trainable quantum walk for ranking nodes based on how perturbations propagate through a network. It learns edge weights, link phases, and node rotations end-to-end while maintaining unitarity. This allows the model to identify which graph components drive system responses by using quantum interference before calculating final scores.