{
  "abstract": "Modeling the response of driven many-body quantum systems from input--output data is difficult: the dynamics are nonlinear, history dependent, and expensive to simulate as system size grows. A paradigmatic case is High-Harmonic Generation~(HHG), where a strong field drives a medium to emit radiation that is highly sensitive to the drive and encodes long-range temporal correlations. We introduce a dissipative quantum reservoir computing~(DQRC) framework that builds a digital twin of such a system, learning its input--output map directly from data while the reservoir---itself a small open quantum system---stays fixed and only a classical readout is trained. We show that a minimal single-qubit reservoir reproduces the HHG response of a substantially larger Ising spin chain, and on a representative benchmark matches and on several metrics surpasses previously reported temporal convolutional and Kolmogorov--Arnold-network models, while using a simpler, physically realizable system. A single fixed reservoir further generalizes across a broad range of drives, indicating that it learns a shared physical response structure rather than memorizing trajectories. These results establish dissipative quantum reservoirs as compact, physically grounded digital twins for nonlinear, memory-dependent quantum dynamics. Code is available at \\href{https://github.com/AI-and-Quantum-Computing/DQuRC}{https://github.com/AI-and-Quantum-Computing/DQuRC}.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2609.36901",
  "author_total": 5,
  "authors": [
    {
      "name": "Abhijit Sen"
    },
    {
      "name": "Bikram Keshari Parida"
    },
    {
      "name": "Shital Chauhan"
    },
    {
      "name": "Mahima Arya"
    },
    {
      "name": "Denys I. Bondar"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/6habw04rbd3n0.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2609.36901",
  "fulltext_url": null,
  "id": "6habw04rbd3n0",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2609.36901",
  "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",
      "cs.AI"
    ],
    [
      "arxiv",
      "quant-ph"
    ],
    [
      "openalex",
      "1702|Neural Networks and Reservoir Computing"
    ]
  ],
  "openalex": "W7214927728",
  "pdf_url": "https://arxiv.org/pdf/2609.36901",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-29",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/6habw04rbd3n0.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:25:31Z",
      "native_id": "2609.36901",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2609.36901"
    }
  ],
  "subjects": [
    {
      "field": "physics",
      "origin": "arxiv:quant-ph",
      "subfield": "quantum"
    }
  ],
  "title": "Digital Twin Modeling of Quantum Dynamical Systems: Dissipative Quantum Reservoir Computing",
  "title_text": "Digital Twin Modeling of Quantum Dynamical Systems: Dissipative Quantum Reservoir Computing",
  "type": "preprint",
  "updated_at": "2026-10-30T12:25:31Z",
  "url": "https://research.draconitic.com/paper/6habw04rbd3n0",
  "venue": "arXiv",
  "versions": []
}