{
  "abstract": "We introduce a mixture-of-experts framework combining sixteen hybrid quantum-classical neural networks with nucleus-dependent classical gating to learn residual corrections to the WS4 nuclear mass model. On a fixed AME2020 test set, the framework reduces the root-mean-square error from 456.22 to 179.83 keV and outperforms a parameter-matched classical counterpart. The learned corrections improve shell-gap systematics near the Sn and Pb shell closures, while analysis of the gating networks identifies shell-related descriptors as the leading inputs. For 66Ca, the model predicts Sn = 0.577 MeV and S2n = -0.252 MeV, allowing two-neutron emission while the one-neutron channel remains closed.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2610.40022",
  "author_total": 6,
  "authors": [
    {
      "name": "Shijiang Ji"
    },
    {
      "name": "Heyi Yang"
    },
    {
      "name": "Zu-Xing Yang"
    },
    {
      "name": "Xiao-Hua Fan"
    },
    {
      "name": "Zhi-Pan Li"
    },
    {
      "name": "Haozhao Liang"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/4fdqncxz8h4vz.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2610.40022",
  "fulltext_url": null,
  "id": "4fdqncxz8h4vz",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2610.40022",
  "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",
      "nucl-th"
    ]
  ],
  "openalex": null,
  "pdf_url": "https://arxiv.org/pdf/2610.40022",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-29",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/4fdqncxz8h4vz.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:36:23Z",
      "native_id": "2610.40022",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2610.40022"
    }
  ],
  "subjects": [
    {
      "field": "physics",
      "origin": "arxiv:nucl-th",
      "subfield": "nuclear"
    }
  ],
  "title": "Learning nuclear mass systematics with hybrid quantum-classical mixtures of experts",
  "title_text": "Learning nuclear mass systematics with hybrid quantum-classical mixtures of experts",
  "type": "preprint",
  "updated_at": "2026-11-01T12:20:45Z",
  "url": "https://research.draconitic.com/paper/4fdqncxz8h4vz",
  "venue": "arXiv",
  "versions": []
}