{
  "abstract": "Predicting and controlling driven quantum systems requires access to dynamics across physical parameters, initial states, and evolution times. A reusable neural model must capture this dependence while preserving unitary structure and maintaining accuracy over long-time coherent evolution. Here we introduce Quantum Evolution Networks (QENs), which map physical parameters and time to complete propagators for periodically driven three-spin registers. Shared networks learn unitary one-period propagators and compatible within-period paths, enabling predictions through known periodic composition without query-specific numerical integration. For transverse driving, the fifth-percentile fidelity, minimized over input states and sampled times, is 0.9947 over 24 periods on 256 held-out configurations. A leading-order analysis of spectrally resolved endpoint residuals predicts multicycle process error more accurately than quadratic or spectrum-averaged estimates. Interventions at matched single-period infidelity produce orders-of-magnitude differences in multicycle infidelity, demonstrating the importance of residual orientation. Compatible component replacements identify how endpoint and within-period approximations limit reconstruction accuracy. These results establish a reusable neural representation of the studied propagator families and connect its learned residual structure to its observed long-time reliability.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2610.32156",
  "author_total": 3,
  "authors": [
    {
      "name": "Puran Li"
    },
    {
      "name": "Condon Lau"
    },
    {
      "name": "Xiu-Hao Deng"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/6021nrnanw7ee.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2610.32156",
  "fulltext_url": null,
  "id": "6021nrnanw7ee",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2610.32156",
  "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",
      "1702|Quantum Computing Algorithms and Architecture"
    ]
  ],
  "openalex": "W7223650939",
  "pdf_url": "https://arxiv.org/pdf/2610.32156",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-23",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/6021nrnanw7ee.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:35:15Z",
      "native_id": "2610.32156",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2610.32156"
    }
  ],
  "subjects": [
    {
      "field": "physics",
      "origin": "arxiv:quant-ph",
      "subfield": "quantum"
    }
  ],
  "title": "Quantum Evolution Networks Learn Long-Time Coherent Dynamics in Driven Spin Registers",
  "title_text": "Quantum Evolution Networks Learn Long-Time Coherent Dynamics in Driven Spin Registers",
  "type": "preprint",
  "updated_at": "2026-10-30T12:35:15Z",
  "url": "https://research.draconitic.com/paper/6021nrnanw7ee",
  "venue": "arXiv",
  "versions": []
}