{
  "abstract": "Recent advances in quantum computing are opening new opportunities for computationally intensive decision problems. This paper studies how quantum computing can improve Monte Carlo tree search (MCTS) in the fixed-confidence setting, where the goal is to identify a near-optimal move in a given game tree with high probability while minimizing query complexity. We first formulate MCTS under a quantum oracle model. We then develop a quantum MCTS algorithm (QMCTS) that combines threshold-based elimination on the search tree with quantum Monte Carlo estimation to reduce the cost of evaluating stochastic leaf values. We establish an instance-dependent lower bound on the query complexity and derive a corresponding upper bound for QMCTS. The lower-bound analysis introduces a new quantum phase-testing result that may also be useful beyond MCTS. We validate the theoretical results through simulation experiments and further demonstrate the feasibility of QMCTS on real quantum hardware.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2609.33132",
  "author_total": 3,
  "authors": [
    {
      "name": "Mingjie Hu"
    },
    {
      "name": "Jian-Qiang Hu"
    },
    {
      "name": "Enlu Zhou"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/ds9dysvne3qt9.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2609.33132",
  "fulltext_url": null,
  "id": "ds9dysvne3qt9",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2609.33132",
  "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",
      "cs.AI"
    ],
    [
      "arxiv",
      "quant-ph"
    ],
    [
      "openalex",
      "1702|Quantum Computing Algorithms and Architecture"
    ]
  ],
  "openalex": "W7214758717",
  "pdf_url": "https://arxiv.org/pdf/2609.33132",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-27",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/ds9dysvne3qt9.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:25:12Z",
      "native_id": "2609.33132",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2609.33132"
    }
  ],
  "subjects": [
    {
      "field": "physics",
      "origin": "arxiv:quant-ph",
      "subfield": "quantum"
    }
  ],
  "title": "Quantum Monte Carlo Tree Search with Fixed Confidence",
  "title_text": "Quantum Monte Carlo Tree Search with Fixed Confidence",
  "type": "preprint",
  "updated_at": "2026-10-30T12:25:12Z",
  "url": "https://research.draconitic.com/paper/ds9dysvne3qt9",
  "venue": "arXiv",
  "versions": []
}