{
  "abstract": "We study short-horizon neonatal seizure prediction on Helsinki-style (HUH) preprocessed EEG feature tables using quantum-inspired reservoir computing. Under strict subject-wise cross-validation on imbalanced preictal--interictal windows, we test whether reservoir constructions shaped after (i) gate-based qubit dynamics and (ii) continuous-variable (bosonic/photonic) dynamics provide a practical sequence encoder relative to strong classical baselines. Both reservoirs are executed here purely in classical simulation on a single GPU; accordingly, we do not claim quantum advantage, but rather evaluate whether quantum-shaped dynamics act as useful, low-footprint random feature maps with a trained linear readout. We compare against Random Forests and RBF--SVMs, and report AUROC, AUPRC, per-subject AUPRC, expected calibration error, and stratified bootstrap confidence intervals. Across our experiments, the CV/bosonic reservoir is consistently competitive and can improve AUPRC over the baselines, while the gate-style reservoir is more sensitive to hyperparameters. These results suggest that quantum-inspired, photonics-motivated reservoirs can be practical sequence encoders for neonatal EEG feature tables under conservative subject-wise evaluation.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2611.00008",
  "author_total": 2,
  "authors": [
    {
      "name": "Krishna Bhatia"
    },
    {
      "name": "Chun-Yu Lin"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/939exrqbtb9j9.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2611.00008",
  "fulltext_url": null,
  "id": "939exrqbtb9j9",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2611.00008",
  "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",
      "cs.LG"
    ],
    [
      "arxiv",
      "physics.med-ph"
    ],
    [
      "arxiv",
      "quant-ph"
    ]
  ],
  "openalex": null,
  "pdf_url": "https://arxiv.org/pdf/2611.00008",
  "pmcid": null,
  "pmid": null,
  "published": "2026-11-01",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/939exrqbtb9j9.ris",
  "sources": [
    {
      "first_seen": "2026-11-03T12:20:59Z",
      "native_id": "2611.00008",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2611.00008"
    }
  ],
  "subjects": [
    {
      "field": "physics",
      "origin": "arxiv:physics.med-ph",
      "subfield": "biological-medical"
    },
    {
      "field": "physics",
      "origin": "arxiv:quant-ph",
      "subfield": "quantum"
    }
  ],
  "title": "Neonatal EEG Seizure Prediction with Quantum Inspired Continuous-Variable (Bosonic) Reservoir Computing",
  "title_text": "Neonatal EEG Seizure Prediction with Quantum Inspired Continuous-Variable (Bosonic) Reservoir Computing",
  "type": "preprint",
  "updated_at": "2026-11-05T15:20:18Z",
  "url": "https://research.draconitic.com/paper/939exrqbtb9j9",
  "venue": "arXiv",
  "versions": []
}