{"ID":6138230,"CreatedAt":"2026-07-09T01:07:32.349475501Z","UpdatedAt":"2026-07-11T12:10:40.064150749Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2607.07336","arxiv_id":"2607.07336","title":"Resource-Efficient Hybrid Quantum Neighborhood Selection for Large-Scale Molecular Diversity Optimization","abstract":"Large-scale combinatorial optimization remains demanding for classical heuristics, particularly when dense Quadratic Unconstrained Binary Optimization (QUBO) formulations induce large memory footprints, high CPU utilization, and long execution times. While near-term quantum processors cannot yet deliver unconditional quantum advantage, hybrid architectures can provide practical value by reducing the resource burden. This paper presents a resource-efficiency study of Hybrid Quantum Neighborhood Selection (HQNS), a framework that decomposes large dense QUBO instances into bounded-width quantum subproblems via stochastic frontier selection. We evaluate HQNS on the Maximum Diversity Subset Selection Problem (MDSSP), focusing on the trade-off between solution quality retention and resource consumption. Benchmarks up to N=1000 candidates show that HQNS preserves 99.9908% of the mean diversity score of an 11-restart parallel Simulated Annealing baseline, while reducing wall-clock time by 94.91%, peak CPU utilization by 64.68%, and peak memory usage by 88.61%. The QPU execution time remains bounded within a 6-7 second envelope across scales, indicating that the quantum component is decoupled from the global QUBO dimension when the frontier size is fixed. These results suggest that HQNS provides a resource-aware pathway for deploying hybrid quantum optimization in practical large-scale settings, serving as an efficient architecture for incorporating near-term quantum processors into classical optimization pipelines.","short_abstract":"Large-scale combinatorial optimization remains demanding for classical heuristics, particularly when dense Quadratic Unconstrained Binary Optimization (QUBO) formulations induce large memory footprints, high CPU utilization, and long execution times. While near-term quantum processors cannot yet deliver unconditional q...","url_abs":"https://arxiv.org/abs/2607.07336","url_pdf":"https://arxiv.org/pdf/2607.07336v1","authors":"[\"Nicolas Mendes de Araujo\",\"Lester de Abreu Faria\"]","published":"2026-07-08T12:25:47Z","proceeding":"quant-ph","tasks":"[\"quant-ph\",\"cs.ET\",\"math.OC\",\"q-bio.BM\"]","methods":"[]","has_code":false}