{
  "abstract": "The Quantum Approximate Optimization Algorithm (QAOA) is a leading variational framework for combinatorial optimization on noisy intermediate-scale quantum hardware, but its practical performance depends strongly on the classical optimizer used to train its variational parameters. This outer-loop optimization is often nonconvex, initialization-sensitive, and costly when repeated across large families of related problem instances. In this work, we propose a Transformer-based intrinsic optimization framework for QAOA, in which the optimizer itself is learned and embedded directly into the hybrid quantum-classical loop. The proposed graph-conditioned Transformer processes problem structure, current QAOA parameters, measurement feedback, and recent optimization history to predict the next variational-parameter update, thereby reformulating instance-wise classical optimization as an amortized learned policy. We develop a mathematical formulation of this intrinsic-optimization perspective and evaluate the method on QAOA-based MaxCut benchmarks across multiple problem settings, with comparisons against representative classical and learned optimization baselines. The results demonstrate that Transformer-based intrinsic optimization can provide a structured and transferable mechanism for improving the classical component of hybrid quantum optimization algorithms.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2609.19392",
  "author_total": 6,
  "authors": [
    {
      "name": "Kuan-Cheng Chen"
    },
    {
      "name": "Xiaotian Xu"
    },
    {
      "name": "Hiromichi Matsuyama"
    },
    {
      "name": "Wei-Hao Huang"
    },
    {
      "name": "Haomu Yuan"
    },
    {
      "name": "Yu Yamashiro"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/4ncxjgj5j73hx.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2609.19392",
  "fulltext_url": null,
  "id": "4ncxjgj5j73hx",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2609.19392",
  "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",
      "quant-ph"
    ],
    [
      "openalex",
      "1702|Quantum Computing Algorithms and Architecture"
    ]
  ],
  "openalex": "W7213611659",
  "pdf_url": "https://arxiv.org/pdf/2609.19392",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-16",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/4ncxjgj5j73hx.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:22:19Z",
      "native_id": "2609.19392",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2609.19392"
    }
  ],
  "subjects": [
    {
      "field": "physics",
      "origin": "arxiv:quant-ph",
      "subfield": "quantum"
    }
  ],
  "title": "Transformers as Intrinsic Optimizers for Quantum Approximate Optimization Algorithm",
  "title_text": "Transformers as Intrinsic Optimizers for Quantum Approximate Optimization Algorithm",
  "type": "preprint",
  "updated_at": "2026-10-30T12:22:19Z",
  "url": "https://research.draconitic.com/paper/4ncxjgj5j73hx",
  "venue": "arXiv",
  "versions": []
}