{
  "abstract": "Quantum machine learning uses quantum states and dynamics to represent and process data. In quantum reservoir computing, the physical system is typically fixed and a classical readout is trained. Inspired by the formation, strengthening and pruning of synapses during biological learning, we propose quantum neuroplastic learning (QNL), which reorganizes the couplings among the qubits of a quantum reservoir, much as experience rewires connections among neurons. Eligibility traces guide local edits to couplings, fields, gate angles and evolution time, and an edit is retained when a statistical bound on its improvement exceeds a complexity cost. We evaluate QNL under restarting and online monitored protocols on short- and long-term memory, nonlinear autoregressive processing and forecasting, and channel equalization tasks. In simulation, QNL enhances memory capacity, develops long-term recall and sustains adaptation across multiple tasks, with the largest benefit for tasks that require extended memory. On the IBM superconducting processor \\texttt{ibm\\_fez}, with six seven-qubit reservoirs running in parallel on 42 qubits (1.28 million shots in total), configurations learned in simulation and frozen before execution reduced the channel-equalization symbol error rate from 9.7\\% to 2.2\\% and achieved a lower error rate than a matched-budget tuned baseline.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2610.34055",
  "author_total": 1,
  "authors": [
    {
      "name": "Mostafizur Rahaman Laskar"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/1a4z1j2b2pz8a.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2610.34055",
  "fulltext_url": null,
  "id": "1a4z1j2b2pz8a",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2610.34055",
  "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": "W7223881887",
  "pdf_url": "https://arxiv.org/pdf/2610.34055",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-25",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/1a4z1j2b2pz8a.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:35:35Z",
      "native_id": "2610.34055",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2610.34055"
    }
  ],
  "subjects": [
    {
      "field": "physics",
      "origin": "arxiv:quant-ph",
      "subfield": "quantum"
    }
  ],
  "title": "Quantum neuroplastic learning for time-series processing",
  "title_text": "Quantum neuroplastic learning for time-series processing",
  "type": "preprint",
  "updated_at": "2026-10-30T12:35:35Z",
  "url": "https://research.draconitic.com/paper/1a4z1j2b2pz8a",
  "venue": "arXiv",
  "versions": []
}