{
  "abstract": "Quantum machine learning has the potential to become a new tool for understanding complex nuclear structures. In this work, we apply a hybrid quantum convolutional neural network (QCNN) to nuclear charge-radius prediction for the first time, aiming to explore the feasibility of quantum machine learning in nuclear-physics data analysis. Based on a classical convolutional neural network (CNN) framework, a small variational quantum convolutional filter is introduced as a quantum feature map to extract local correlations on the nuclear chart. The QCNN shows promising predictive accuracy and training stability, and provides a reliable description of the charge-radius evolution along several representative isotopic chains. These results support further investigation of quantum convolutional architectures for nuclear-structure data analysis.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2609.12393",
  "author_total": 7,
  "authors": [
    {
      "name": "Jinzhe Wu"
    },
    {
      "name": "Jianping Zhao"
    },
    {
      "name": "Tianshuai Shang"
    },
    {
      "name": "Yanhua Lu"
    },
    {
      "name": "Yundong Wang"
    },
    {
      "name": "Jian Li"
    },
    {
      "name": "Haozhao Liang"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/azje374srrcpz.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2609.12393",
  "fulltext_url": null,
  "id": "azje374srrcpz",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2609.12393",
  "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-ex"
    ],
    [
      "arxiv",
      "nucl-th"
    ],
    [
      "openalex",
      "3106|Nuclear physics research studies"
    ]
  ],
  "openalex": "W7212919623",
  "pdf_url": "https://arxiv.org/pdf/2609.12393",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-11",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/azje374srrcpz.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:21:07Z",
      "native_id": "2609.12393",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2609.12393"
    }
  ],
  "subjects": [
    {
      "field": "physics",
      "origin": "arxiv:nucl-ex",
      "subfield": "nuclear"
    }
  ],
  "title": "Quantum convolutional neural network for predicting nuclear charge radii",
  "title_text": "Quantum convolutional neural network for predicting nuclear charge radii",
  "type": "preprint",
  "updated_at": "2026-10-30T12:21:07Z",
  "url": "https://research.draconitic.com/paper/azje374srrcpz",
  "venue": "arXiv",
  "versions": []
}