{
  "abstract": "Quantum algorithms can offer substantial computational speed-ups, yet these advantages may disappear once the cost of structure-agnostic classical data encoding is taken into account. Real-world problem instances, however, often possess rich internal structure. This raises a fundamental question: can such structure be harnessed to make quantum speed-ups survive end-to-end cost accounting? Here we show that it can. We introduce a general framework for structure-aware quantum data encoding that compiles compact recursive descriptions of problem structure into efficient state-preparation circuits, translating structural information directly into reduced encoding complexity. For the uncapacitated facility-location problem, structure-agnostic encoding admits a classical dequantization that eliminates the quadratic quantum speed-up, whereas exploiting the underlying problem structure restores this advantage even when state-preparation costs are included. More broadly, the same framework prepares structured quantum states for combinatorial optimization that capture non-trivial relations among constraints, including group balance, conflicts and synergistic rewards, extending previously reported super-polynomial quantum advantages to broader classes of objectives. These results establish exploitable problem structure as a computational resource for quantum algorithms and provide a systematic route towards realizing quantum advantage in data-intensive problems.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2609.40105",
  "author_total": 6,
  "authors": [
    {
      "name": "Qifan Jiang"
    },
    {
      "name": "Xiao-Ming Zhang"
    },
    {
      "name": "Debin Xiang"
    },
    {
      "name": "Xiao Yuan"
    },
    {
      "name": "Liqiang Lu"
    },
    {
      "name": "Jianwei Yin"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/2zpqcc6x0ebsh.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2609.40105",
  "fulltext_url": null,
  "id": "2zpqcc6x0ebsh",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2609.40105",
  "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": "W7215069976",
  "pdf_url": "https://arxiv.org/pdf/2609.40105",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-29",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/2zpqcc6x0ebsh.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:36:28Z",
      "native_id": "2609.40105",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2609.40105"
    }
  ],
  "subjects": [
    {
      "field": "physics",
      "origin": "arxiv:quant-ph",
      "subfield": "quantum"
    }
  ],
  "title": "Harnessing problem structure for end-to-end quantum speed-ups",
  "title_text": "Harnessing problem structure for end-to-end quantum speed-ups",
  "type": "preprint",
  "updated_at": "2026-11-01T12:20:44Z",
  "url": "https://research.draconitic.com/paper/2zpqcc6x0ebsh",
  "venue": "arXiv",
  "versions": []
}