{
  "abstract": "The control of open-system dynamics provides a powerful mechanism for using quantum information to process sequential tasks. While quantum reservoir computing typically relies on Markovian dissipation to process sequential data, the computational role of non-Markovian memory effects remains largely unexplored. We introduce a framework for quantum reservoir computing where non-Markovianity is explicitly modeled and regulated using fractional derivatives. By employing fractional time subordination, we generate tunable, heavy-tailed relaxation dynamics that govern the information backflow between the system and its environment. Non-Markovian information backflow regulates the overall temporal retention of the system, maximizing short-term linear memory capacity. Within an optimal operating regime, fractional non-Markovianity redistributes the reservoir memory towards recent inputs, improving both linear memory capacity and nonlinear prediction accuracy at short delays, at the expense of long-range temporal retention. System-environment interaction proves being not merely a requirement for quantum reservoir computing, but an active resource that embeds memory retention directly into the quantum evolution.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2609.40172",
  "author_total": 3,
  "authors": [
    {
      "name": "Luca Nigro"
    },
    {
      "name": "Francesco Monzani"
    },
    {
      "name": "Enrico Prati"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/89b9v8pyzrndt.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2609.40172",
  "fulltext_url": null,
  "id": "89b9v8pyzrndt",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2609.40172",
  "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|Neural Networks and Reservoir Computing"
    ]
  ],
  "openalex": "W7215116013",
  "pdf_url": "https://arxiv.org/pdf/2609.40172",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-30",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/89b9v8pyzrndt.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:25:49Z",
      "native_id": "2609.40172",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2609.40172"
    }
  ],
  "subjects": [
    {
      "field": "physics",
      "origin": "arxiv:quant-ph",
      "subfield": "quantum"
    }
  ],
  "title": "Non-Markovian dissipation as a resource for quantum reservoir computing",
  "title_text": "Non-Markovian dissipation as a resource for quantum reservoir computing",
  "type": "preprint",
  "updated_at": "2026-10-30T12:25:49Z",
  "url": "https://research.draconitic.com/paper/89b9v8pyzrndt",
  "venue": "arXiv",
  "versions": []
}