{
  "abstract": "We show that a quantum reservoir computer optimized with its quantum Fisher information (QFI) has a quantum advantage over a classical reservoir of equal readout cost, provided by a quantum-information advantage: optimizing the quantum parameters with the QFI metric. On Mackey-Glass forecasting, a driven Jaynes-Cummings reservoir with one to three atoms beats a tuned echo-state network at every size from 4 to 30 trained weights, by 25-69% in held-out error, and is ahead of 99.5% of 10,000 random networks. After grid and Bayesian searches saturate, the QFI-aware stage lowers the error by 29-51% at 12-30 weights, beyond equal-budget black-box search or classical-parameter gradients alone, and at 30 weights turns a loss into a win. We then derive a fundamental QFI bound on the first-order rate of change of the retrained loss and factorize the gap to this bound into five link efficiencies that identify the physical mechanism limiting optimization.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2610.14454",
  "author_total": 2,
  "authors": [
    {
      "name": "Phi Hung Nguyen"
    },
    {
      "name": "Daniel Soh"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/dge0vdxd3nt3x.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2610.14454",
  "fulltext_url": null,
  "id": "dge0vdxd3nt3x",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2610.14454",
  "language": null,
  "licence": "CC0-1.0",
  "licence_label": "CC0",
  "licence_source": "article",
  "licence_url": "http://creativecommons.org/publicdomain/zero/1.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "quant-ph"
    ],
    [
      "openalex",
      "1702|Neural Networks and Reservoir Computing"
    ]
  ],
  "openalex": "W7221825606",
  "pdf_url": "https://arxiv.org/pdf/2610.14454",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-10",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/dge0vdxd3nt3x.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:29:06Z",
      "native_id": "2610.14454",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2610.14454"
    }
  ],
  "subjects": [
    {
      "field": "physics",
      "origin": "arxiv:quant-ph",
      "subfield": "quantum"
    }
  ],
  "title": "Quantum-Fisher-information-aware optimization yields a quantum reservoir advantage over tuned classical reservoirs",
  "title_text": "Quantum-Fisher-information-aware optimization yields a quantum reservoir advantage over tuned classical reservoirs",
  "type": "preprint",
  "updated_at": "2026-10-30T12:29:06Z",
  "url": "https://research.draconitic.com/paper/dge0vdxd3nt3x",
  "venue": "arXiv",
  "versions": []
}