{
  "abstract": "Variational quantum algorithms are often limited by barren plateaus: gradients vanish as circuit size and depth increase, making quantum neural networks difficult to train. We propose Q-Capsule, a localized capsule-based quantum neural architecture that mitigates this problem through register partitioning, local readout, sparse inter-capsule coupling, trainable data re-uploading, and Quantum Fisher Information Matrix (QFIM)-guided adaptive depth growth. By restricting the dominant support of each observable to a small capsule and controlling inter-capsule entanglement, Q-Capsule preserves useful gradient signals while retaining communication between local quantum representations. As the register width increases, Q-Capsule consistently maintains stable gradient variance, whereas globally entangling baselines exhibit exponential suppression with a log-gradient-variance slope near -ln 2 per qubit. Q-Capsule also produces more structured optimization landscapes, higher parameter efficiency, improved robustness to depolarizing noise, and lower measurement requirements. Its adaptive policy achieves 98.1% accuracy on binary classification and 97.7% on four-class classification, while using approximately 73% fewer two-qubit gates than the fixed-deep model on the multiclass task.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2610.11261",
  "author_total": 7,
  "authors": [
    {
      "name": "Awal Ahmed Fime"
    },
    {
      "name": "Tasfia Zaman Samiha"
    },
    {
      "name": "Saika Zaman"
    },
    {
      "name": "Dimitris Pados"
    },
    {
      "name": "George Sklivanitis"
    },
    {
      "name": "Abdur R. Shahid"
    },
    {
      "name": "Ahmed Imteaj"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/a4ayrnyjgz2bh.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2610.11261",
  "fulltext_url": null,
  "id": "a4ayrnyjgz2bh",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2610.11261",
  "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",
      "quant-ph"
    ],
    [
      "openalex",
      "1702|Quantum Computing Algorithms and Architecture"
    ]
  ],
  "openalex": "W7221183065",
  "pdf_url": "https://arxiv.org/pdf/2610.11261",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-08",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/a4ayrnyjgz2bh.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:27:54Z",
      "native_id": "2610.11261",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2610.11261"
    }
  ],
  "subjects": [
    {
      "field": "physics",
      "origin": "arxiv:quant-ph",
      "subfield": "quantum"
    }
  ],
  "title": "Q-Capsule: A Localized Capsule-Based Quantum Neural Architecture for Barren Plateau Mitigation",
  "title_text": "Q-Capsule: A Localized Capsule-Based Quantum Neural Architecture for Barren Plateau Mitigation",
  "type": "preprint",
  "updated_at": "2026-10-30T12:27:54Z",
  "url": "https://research.draconitic.com/paper/a4ayrnyjgz2bh",
  "venue": "arXiv",
  "versions": []
}