{
  "abstract": "Quantum-classical auxiliary-field quantum Monte Carlo (QC-AFQMC) uses a trial state prepared on a quantum computer to improve the accuracy of the classical AFQMC algorithm. State-of-the-art QC-AFQMC represents this state with matchgate shadows, so every trial-walker overlap and local energy must be averaged over many snapshots, making this classical computation a bottleneck. Here we distill the matchgate shadow data into a symmetry-constrained, noise-aware, autoregressive transformer neural quantum state (NQS). Used as the AFQMC trial state, the NQS evaluates overlaps, force biases, and local energies without repeatedly summing over the matchgate shadows. For a nickel-catalyzed oxidative-addition reaction, the noise-awareness of our NQS design successfully recovers the ideal-upCCD AFQMC barrier and reaction energy within statistical uncertainty, while standard QC-AFQMC execution with the same shadow data yields qualitatively inaccurate results as a result of hardware noise. Since the shadow data are needed only for training, the estimated classical cost for the nickel system falls by approximately 99% relative to our previous QC-AFQMC implementation.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2611.08129",
  "author_total": 7,
  "authors": [
    {
      "name": "Justin Provazza"
    },
    {
      "name": "Oliver Knitter"
    },
    {
      "name": "Joshua Goings"
    },
    {
      "name": "Evgeny Epifanovsky"
    },
    {
      "name": "Chi Chen"
    },
    {
      "name": "Martin Roetteler"
    },
    {
      "name": "Luning Zhao"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/6qy1hv7jq0cc2.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2611.08129",
  "fulltext_url": null,
  "id": "6qy1hv7jq0cc2",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2611.08129",
  "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": null,
  "pdf_url": "https://arxiv.org/pdf/2611.08129",
  "pmcid": null,
  "pmid": null,
  "published": "2026-11-05",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/6qy1hv7jq0cc2.ris",
  "sources": [
    {
      "first_seen": "2026-11-06T18:21:30Z",
      "native_id": "2611.08129",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2611.08129"
    }
  ],
  "subjects": [
    {
      "field": "physics",
      "origin": "arxiv:quant-ph",
      "subfield": "quantum"
    }
  ],
  "title": "Accelerating Quantum-Classical Auxiliary-Field Quantum Monte Carlo with Shadow-Trained Neural Quantum States",
  "title_text": "Accelerating Quantum-Classical Auxiliary-Field Quantum Monte Carlo with Shadow-Trained Neural Quantum States",
  "type": "preprint",
  "updated_at": "2026-11-09T00:20:41Z",
  "url": "https://research.draconitic.com/paper/6qy1hv7jq0cc2",
  "venue": "arXiv",
  "versions": []
}