{
  "abstract": "Feedback-based adaptive quantum optimization (FALQON) is a promising approach for combinatorial optimization on noisy intermediate-scale quantum devices, but prior studies report that standard fixed-parameter schedules can require relatively deep circuits to reach strong solution quality. We study Optimal FALQON, a layer-wise formulation that treats the time-step $δ_k$ and scaling parameter $w_k$ as classical decision variables optimized per layer. The benchmark uses all 94 non-isomorphic 3-regular graphs with 12 vertices under 8192-shot simulation and compares standard FALQON, Optimal FALQON, and QAOA/QAOA-MA Powell runs warm-started from FALQON-derived schedules. Across approximation ratio, success probability, evaluation-normalized efficiency, and layer-normalized efficiency, Optimal FALQON substantially improves on standard FALQON, and Optimal-FALQON warm starts substantially improve on standard-FALQON warm starts.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2605.08332",
  "author_total": 2,
  "authors": [
    {
      "name": "Michael Mancini"
    },
    {
      "name": "Shabnam Sodagari"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/4sfnbd0nje5z2.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2605.08332",
  "fulltext_url": null,
  "id": "4sfnbd0nje5z2",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2605.08332",
  "language": null,
  "licence": "CC-BY-NC-ND-4.0",
  "licence_label": "CC BY-NC-ND 4.0",
  "licence_source": "article",
  "licence_url": "http://creativecommons.org/licenses/by-nc-nd/4.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "cs.AI"
    ],
    [
      "arxiv",
      "quant-ph"
    ],
    [
      "openalex",
      "1702|Quantum Computing Algorithms and Architecture"
    ]
  ],
  "openalex": "W7160895315",
  "pdf_url": "https://arxiv.org/pdf/2605.08332",
  "pmcid": null,
  "pmid": null,
  "published": "2026-11-03",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/4sfnbd0nje5z2.ris",
  "sources": [
    {
      "first_seen": "2026-11-04T15:21:24Z",
      "native_id": "2605.08332",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2605.08332"
    }
  ],
  "subjects": [
    {
      "field": "physics",
      "origin": "arxiv:quant-ph",
      "subfield": "quantum"
    }
  ],
  "title": "Optimal FALQON for Quantum Approximate Optimization via Layer-wise Parameter Tuning",
  "title_text": "Optimal FALQON for Quantum Approximate Optimization via Layer-wise Parameter Tuning",
  "type": "preprint",
  "updated_at": "2026-11-06T18:21:01Z",
  "url": "https://research.draconitic.com/paper/4sfnbd0nje5z2",
  "venue": "arXiv",
  "versions": []
}