{
  "abstract": "Feedback-based ALgorithm for Quantum OptimizatioN (FALQON) is a hybrid quantum-classical algorithm for solving combinatorial optimization problems, which circumvents classical parameter optimization but requires a deep quantum circuit. To reduce circuit depth, Arai et al. proposed second-order FALQON (SO-FALQON), achieving the best depth reduction among existing approaches. However, SO-FALQON brings a 2.3 times per-step measurement overhead, as it needs to calculate an additional second-order control coefficient. In this paper, inspired by the Backtracking Line Search (BLS) theory, we propose another method called BLS-FALQON, which not only reduces circuit depth to a comparable extent, but also achieves fewer measurements than SO-FALQON. Numerical simulations on max-cut problem with 8 to 20 vertices demonstrate that BLS-FALQON reduces the total measurement count by 37.7% compared to SO-FALQON, while maintaining a comparable circuit depth. Furthermore, we conduct real quantum hardware experiments on the Tianyan-176 quantum computer, which uses the zuchongzhi2 superconducting quantum processor, confirming that BLS-FALQON remains effective under real quantum hardware conditions.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2609.32433",
  "author_total": 8,
  "authors": [
    {
      "name": "Zi-Wen Huang"
    },
    {
      "name": "Jia-Cheng Fan"
    },
    {
      "name": "Xiao-Hui Ni"
    },
    {
      "name": "Su-Juan Qin"
    },
    {
      "name": "Xiao-Kai Hou"
    },
    {
      "name": "Wei Huang"
    },
    {
      "name": "Bing-Jie Xu"
    },
    {
      "name": "Fei Gao"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/3d995dwg21xh8.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2609.32433",
  "fulltext_url": null,
  "id": "3d995dwg21xh8",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2609.32433",
  "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": "W7214802870",
  "pdf_url": "https://arxiv.org/pdf/2609.32433",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-26",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/3d995dwg21xh8.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:25:12Z",
      "native_id": "2609.32433",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2609.32433"
    }
  ],
  "subjects": [
    {
      "field": "physics",
      "origin": "arxiv:quant-ph",
      "subfield": "quantum"
    }
  ],
  "title": "Feedback-based quantum optimization with low depth and measurement",
  "title_text": "Feedback-based quantum optimization with low depth and measurement",
  "type": "preprint",
  "updated_at": "2026-10-30T12:25:12Z",
  "url": "https://research.draconitic.com/paper/3d995dwg21xh8",
  "venue": "arXiv",
  "versions": []
}