{
  "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_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2610.41788",
  "author_total": 6,
  "authors": [
    {
      "name": "Onur Boyar"
    },
    {
      "name": "Seiji Takeda"
    },
    {
      "name": "Akihiro Kosugi"
    },
    {
      "name": "Indra Priyadarsini S"
    },
    {
      "name": "Lisa Hamada"
    },
    {
      "name": "Sina Klampt"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/9t18dac1hhknd.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2610.41788",
  "fulltext_url": null,
  "id": "9t18dac1hhknd",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2610.41788",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "http://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "physics.comp-ph"
    ],
    [
      "arxiv",
      "quant-ph"
    ]
  ],
  "openalex": null,
  "pdf_url": "https://arxiv.org/pdf/2610.41788",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-30",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/9t18dac1hhknd.ris",
  "sources": [
    {
      "first_seen": "2026-11-02T12:21:15Z",
      "native_id": "2610.41788",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2610.41788"
    }
  ],
  "subjects": [
    {
      "field": "physics",
      "origin": "arxiv:physics.comp-ph",
      "subfield": "computational"
    },
    {
      "field": "physics",
      "origin": "arxiv:quant-ph",
      "subfield": "quantum"
    }
  ],
  "title": "Query-Efficient Quantum Multi-Objective Optimization for Materials Configuration Search via Bayesian Weight Selection",
  "title_text": "Query-Efficient Quantum Multi-Objective Optimization for Materials Configuration Search via Bayesian Weight Selection",
  "type": "preprint",
  "updated_at": "2026-11-04T15:20:51Z",
  "url": "https://research.draconitic.com/paper/9t18dac1hhknd",
  "venue": "arXiv",
  "versions": []
}