{
  "abstract": "Many optimization problems reveal inexpensive structural information before requiring costly final evaluation, whereas the conventional quantum approximate optimization algorithm (QAOA) applies a single aggregate objective throughout. We introduce the learned-projector quantum alternating operator ansatz (LP-QAOA), a multistage protocol that freezes optimized circuits and uses their output states to define later projector mixers. We prove a stability theorem for LP-QAOA that bounds the propagation of approximation errors through successive frozen stages. Our analysis also shows how learned-projector mixing avoids the high-order tunnelling suppression of local mixers. We conduct state-vector simulations on block-constrained spin tiling (BCST) instances, where LP-QAOA achieves higher probabilities of sampling the optimum, lower estimated logical-resource requirements, and better trainability than the tested baselines. A supporting stochastic block model experiment extends LP-QAOA to optimization problems with soft hierarchical structure. Our results highlight the potential of LP-QAOA to improve variational quantum optimization by exploiting hierarchical problem structure.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2609.28888",
  "author_total": 3,
  "authors": [
    {
      "name": "Kangyun Zhou"
    },
    {
      "name": "Dong An"
    },
    {
      "name": "Jin-Peng Liu"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/e2vstqd8s0hjb.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2609.28888",
  "fulltext_url": null,
  "id": "e2vstqd8s0hjb",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2609.28888",
  "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": "W7214385777",
  "pdf_url": "https://arxiv.org/pdf/2609.28888",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-24",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/e2vstqd8s0hjb.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:24:11Z",
      "native_id": "2609.28888",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2609.28888"
    }
  ],
  "subjects": [
    {
      "field": "physics",
      "origin": "arxiv:quant-ph",
      "subfield": "quantum"
    }
  ],
  "title": "Learned-projector QAOA for hierarchical optimization",
  "title_text": "Learned-projector QAOA for hierarchical optimization",
  "type": "preprint",
  "updated_at": "2026-10-30T12:24:11Z",
  "url": "https://research.draconitic.com/paper/e2vstqd8s0hjb",
  "venue": "arXiv",
  "versions": []
}