{
  "abstract": "Quantum reservoir computing (QRC) uses quantum dynamics to represent input histories for prediction through a trained classical readout. Discrete time crystals (DTCs) exhibit robust subharmonic responses under periodic driving, and previous work has used their dynamics to construct DTC-QRC. Here we construct a DTC-based reservoir architecture to predict molecular properties from structural and dynamical observations. Coherent Floquet evolution processes local molecular graph events and surface-hopping frames, while controlled reset regulates the contribution of earlier inputs. Measurements at the end of each input sequence yield a feature vector of fixed dimension. Trained classical decoders use this vector for inhibitor-activity and blood--brain-barrier permeability classification and electronic-gap forecasting, while the reservoir parameters remain fixed during training. With matched input lengths and output widths, DTC-QRC outperforms echo-state networks on long-prefix graph classification and the studied ethene gap forecasting tasks. Dephasing lowers performance in both applications, consistent with a role for coherent propagation. Experiments on the Quafu superconducting quantum cloud platform show that pair observables retain task information under device noise. The architecture provides a common framework for molecular screening and time-resolved property prediction using quantum reservoir computing.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2609.11071",
  "author_total": 8,
  "authors": [
    {
      "name": "Luofei Wang"
    },
    {
      "name": "Da Zhang"
    },
    {
      "name": "Congren Wang"
    },
    {
      "name": "Yiming Li"
    },
    {
      "name": "Yuxiao Yang"
    },
    {
      "name": "Xuan Zhang"
    },
    {
      "name": "Xuefeng Cui"
    },
    {
      "name": "Zhang-Qi Yin"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/17hqkw5b5kza4.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2609.11071",
  "fulltext_url": null,
  "id": "17hqkw5b5kza4",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2609.11071",
  "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.LG"
    ],
    [
      "arxiv",
      "quant-ph"
    ],
    [
      "openalex",
      "1702|Neural Networks and Reservoir Computing"
    ]
  ],
  "openalex": "W7212301555",
  "pdf_url": "https://arxiv.org/pdf/2609.11071",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-10",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/17hqkw5b5kza4.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:20:26Z",
      "native_id": "2609.11071",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2609.11071"
    }
  ],
  "subjects": [
    {
      "field": "physics",
      "origin": "arxiv:quant-ph",
      "subfield": "quantum"
    }
  ],
  "title": "Coherent Floquet quantum reservoirs for molecular property prediction",
  "title_text": "Coherent Floquet quantum reservoirs for molecular property prediction",
  "type": "preprint",
  "updated_at": "2026-10-30T12:20:26Z",
  "url": "https://research.draconitic.com/paper/17hqkw5b5kza4",
  "venue": "arXiv",
  "versions": []
}