analiza aiqbench
The paper swaps random weight sampling in QAMOO for a Gaussian-process surrogate with expected-hypervolume-improvement selection, cutting scalarized QAOA solves by a median of about 33% at matched Pareto hypervolume. The result is on a simulated solver with small instances (10 to 12 sites), so it measures query efficiency under a costly, noisy solver, not quantum advantage or hardware performance. For readers, the practical takeaway is that classical Bayesian optimization can make expensive quantum-solver calls go further, and the authors flag the transfer to real alloy data and hardware as open.
Query-Efficient Quantum Multi-Objective Optimization for Materials Configuration Search via Bayesian Weight Selection
arXiv:2610.41788v1 Announce Type: cross Abstract: Materials design is a multi-objective problem, and recent advances in quantum optimization make it possible to bring quantum solvers to bear on such problems. The realistic near-term win, though, is not raw quantum advantage but making an expensive, noisy quantum solver usable by spending its calls wisely. Quantum Approximate Multi-Objective Optimization (QAMOO) traces a Pareto front by solving a family of scalarized Ising problems, one per objective-weight vector, with QAOA --- but chooses those weights \emph{non-adaptively}, sampling them at random, blind to what it has already found. We recast weight selection as \emph{multi-objective Bayesian optimization}: our method, BW-QAMOO (Bayes-Weighted QAMOO), picks each next weight by expected hypervolume improvement (EHVI) under a Gaussian-process (GP) surrogate over the weight-to-objective map. On a frustrated alloy-configuration benchmark across $m\in\{3,4\}$ objectives and $n\in\{10,11,12\}$ sites, BW-QAMOO reaches the same Pareto hypervolume as QAMOO's random sampling with a median 33% fewer scalarized solves across the 90/95\% targets and 60--74\% fewer than a uniform-grid baseline), on a genuine (simulated) QAOA solver; a paired Wilcoxon test confirms the saving is significant, and the advantage grows with the objective count m as the weight simplex expands and blind sampling wastes more of the budget. This is a \emph{query-efficiency} result, and we close with the open challenges on the path to real alloy data and hardware.
Abstract:Materials design is a multi-objective problem, and recent advances in quantum optimization make it possible to bring quantum solvers to bear on such problems. The realistic near-term win, though, is not raw quantum advantage but making an expensive, noisy quantum solver usable by spending its calls wisely. Quantum Approximate Multi-Objective Optimization (QAMOO) traces a Pareto front by solving a family of scalarized Ising problems, one per objective-weight vector, with QAOA --- but chooses those weights \emph{non-adaptively}, sampling them at random, blind to what it has already found. We recast weight selection as \emph{multi-objective Bayesian optimization}: our method, BW-QAMOO (Bayes-Weighted QAMOO), picks each next weight by expected hypervolume improvement (EHVI) under a Gaussian-process (GP) surrogate over the weight-to-objective map. On a frustrated alloy-configuration benchmark across $m\in\{3,4\}$ objectives and $n\in\{10,11,12\}$ sites, BW-QAMOO reaches the same Pareto hypervolume as QAMOO's random sampling with a median 33% fewer scalarized solves across the 90/95\% targets and 60--74\% fewer than a uniform-grid baseline), on a genuine (simulated) QAOA solver; a paired Wilcoxon test confirms the saving is significant, and the advantage grows with the objective count m as the weight simplex expands and blind sampling wastes more of the budget. This is a \emph{query-efficiency} result, and we close with the open challenges on the path to real alloy data and hardware.
Computational Physics (physics.comp-ph); Quantum Physics (quant-ph)
arXiv:2610.41788 [physics.comp-ph]
(or arXiv:2610.41788v1 [physics.comp-ph] for this version)
https://doi.org/10.48550/arXiv.2610.41788 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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