{
  "abstract": "Multi-objective combinatorial optimization requires identifying Pareto-optimal trade-off solutions among conflicting objectives, often making it more demanding than its single-objective counterpart. Although quantum multi-objective optimization methods have begun to emerge, most existing quantum optimization workflows are still built around single-objective or fixed-scalarization settings. Building on existing weighted-sum QAOA approaches to quantum multi-objective optimization, we propose QEMOO, a quantum-enhanced multi-objective optimization framework that combines Pareto-based selection and warm-started QAOA sampling in a multi-round protocol under the same total shot budget. We further introduce a PBI-inspired adaptive direction-update scheme to improve coverage in strongly conflicting benchmark regimes. Across three benchmark stages, QEMOO improves Pareto-front hypervolume over the single-pass weighted-sum QAOA baseline under matched shot budgets, suggesting a practical route toward shot-efficient quantum-assisted multi-objective optimization and its future applications.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2607.18848",
  "author_total": 4,
  "authors": [
    {
      "name": "Maolin Luo"
    },
    {
      "name": "Jiapei Zhuang"
    },
    {
      "name": "Zuoheng Zou"
    },
    {
      "name": "Man-Hong Yung"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/anzxfnbevfbmr.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2607.18848",
  "fulltext_url": null,
  "id": "anzxfnbevfbmr",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2607.18848",
  "language": null,
  "licence": "LicenseRef-arXiv-nonexclusive-1.0",
  "licence_label": "arXiv licence",
  "licence_source": "article",
  "licence_url": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "quant-ph"
    ],
    [
      "openalex",
      "1702|Quantum Computing Algorithms and Architecture"
    ]
  ],
  "openalex": "W7170044007",
  "pdf_url": "https://arxiv.org/pdf/2607.18848",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-02",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/anzxfnbevfbmr.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:18:16Z",
      "native_id": "2607.18848",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2607.18848"
    }
  ],
  "subjects": [
    {
      "field": "physics",
      "origin": "arxiv:quant-ph",
      "subfield": "quantum"
    }
  ],
  "title": "Quantum-Enhanced Multi-Objective Optimization",
  "title_text": "Quantum-Enhanced Multi-Objective Optimization",
  "type": "preprint",
  "updated_at": "2026-10-30T12:18:16Z",
  "url": "https://research.draconitic.com/paper/anzxfnbevfbmr",
  "venue": "arXiv",
  "versions": []
}