The results identify localized quantum interference as a resource-efficient proposal mechanism for computational quantum optimization.
Abstract
Quantum Interference Proposal Search (QIPS) uses seed-conditioned quantum circuits to generate localized interference patterns as finite-shot proposal distributions for QUBO/Ising optimization. Candidate $n_b$-bit strings are sampled from these distributions, scored classically and used to update an elite frontier of low-energy solutions. QIPS uses a fixed two-layer gate-based circuit architecture with 100 shots per circuit while the Hilbert-space dimension grows as $2^{n_b}$. Across six benchmark families with $18 \le n_b \le 29$, QIPS maintains competitive progress relative to a matched classical control that preserves the same search loop, frontier update rule and proposal budget, with total proposals proportional to $n_b$. Performance is assessed using top-$K$ coverage, hit rate, multiplicity, Hilbert-space coverage and dyadic-rank metrics. The results identify localized quantum interference as a resource-efficient proposal mechanism for computational quantum optimization.
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