{
  "abstract": "Bayesian inference enables self-consistent plasma parameter inversion in tokamak integrated data analysis by fusing multi-source diagnostics. However, under nonlinear Gaussian likelihoods, finding the maximum a posteriori (MAP) estimate typically relies on sampling methods whose high computational overhead precludes real-time plasma control. To address this bottleneck, we propose a Bayesian inversion framework using a Coherent Ising Machine (CIM)-a quantum computing architecture based on coupled degenerate optical parametric oscillators (DOPOs). With native all-to-all connectivity, the CIM directly maps the negative log-posterior potential onto a spin Hamiltonian and leverages quantum vacuum fluctuations for rapid state-space search during pitchfork bifurcation. Validated on plasma current profile inversion for the EAST tokamak, this method achieves a single-run solution time of approximately 1 ms. Although current qubit limits necessitate a block-wise iterative scheme that prevents full real-time execution today, rapidly scaling quantum hardware offers a clear pathway toward real-time tokamak control.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2610.23507",
  "author_total": 21,
  "authors": [
    {
      "name": "Zijie Liu"
    },
    {
      "name": "Wenchao Liu"
    },
    {
      "name": "Yao Huang"
    },
    {
      "name": "Kai Wen"
    },
    {
      "name": "Yuehang Wang"
    },
    {
      "name": "Wenxin Li"
    },
    {
      "name": "Zhengping Luo"
    },
    {
      "name": "Anzhe Jiang"
    },
    {
      "name": "Wangyi Rui"
    },
    {
      "name": "Hongdong Zhu"
    },
    {
      "name": "Kai Wu"
    },
    {
      "name": "Qi Gao"
    },
    {
      "name": "Bihao Guo"
    },
    {
      "name": "Jingjing Wang"
    },
    {
      "name": "Shuai Li"
    },
    {
      "name": "Yin Ma"
    },
    {
      "name": "Dalong Chen"
    },
    {
      "name": "Hai Wei"
    },
    {
      "name": "Qiping Yuan"
    },
    {
      "name": "Bingjia Xiao"
    },
    {
      "name": "Jiangang Li"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/2xbgw7g7n8hgd.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2610.23507",
  "fulltext_url": null,
  "id": "2xbgw7g7n8hgd",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2610.23507",
  "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",
      "quant-ph"
    ],
    [
      "openalex",
      "3106|Magnetic confinement fusion research"
    ]
  ],
  "openalex": "W7222952440",
  "pdf_url": "https://arxiv.org/pdf/2610.23507",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-19",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/2xbgw7g7n8hgd.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:30:52Z",
      "native_id": "2610.23507",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2610.23507"
    }
  ],
  "subjects": [
    {
      "field": "physics",
      "origin": "arxiv:quant-ph",
      "subfield": "quantum"
    }
  ],
  "title": "Solving Large-Scale Parameter Inference in Controllable Nuclear Fusion via Coherent Ising Machines and Block Coordinate Descent",
  "title_text": "Solving Large-Scale Parameter Inference in Controllable Nuclear Fusion via Coherent Ising Machines and Block Coordinate Descent",
  "type": "preprint",
  "updated_at": "2026-10-30T12:33:41Z",
  "url": "https://research.draconitic.com/paper/2xbgw7g7n8hgd",
  "venue": "arXiv",
  "versions": []
}