Solving Large-Scale Parameter Inference in Controllable Nuclear Fusion via Coherent Ising Machines and Block Coordinate Descent
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.
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BibTeX
@misc{liu2026solving,
title = {{Solving Large-Scale Parameter Inference in Controllable Nuclear Fusion via Coherent Ising Machines and Block Coordinate Descent}},
author = {Zijie Liu and Wenchao Liu and Yao Huang and Kai Wen and Yuehang Wang and Wenxin Li and Zhengping Luo and Anzhe Jiang and Wangyi Rui and Hongdong Zhu and Kai Wu and Qi Gao and Bihao Guo and Jingjing Wang and Shuai Li and Yin Ma and Dalong Chen and Hai Wei and Qiping Yuan and Bingjia Xiao and Jiangang Li},
howpublished = {arXiv},
year = {2026},
month = oct,
doi = {10.48550/arxiv.2610.23507},
eprint = {2610.23507},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2610.23507},
}
RIS
TY - UNPB
TI - Solving Large-Scale Parameter Inference in Controllable Nuclear Fusion via Coherent Ising Machines and Block Coordinate Descent
AU - Zijie Liu
AU - Wenchao Liu
AU - Yao Huang
AU - Kai Wen
AU - Yuehang Wang
AU - Wenxin Li
AU - Zhengping Luo
AU - Anzhe Jiang
AU - Wangyi Rui
AU - Hongdong Zhu
AU - Kai Wu
AU - Qi Gao
AU - Bihao Guo
AU - Jingjing Wang
AU - Shuai Li
AU - Yin Ma
AU - Dalong Chen
AU - Hai Wei
AU - Qiping Yuan
AU - Bingjia Xiao
AU - Jiangang Li
PY - 2026
DA - 2026/10/19
PB - arXiv
DO - 10.48550/arxiv.2610.23507
UR - https://arxiv.org/abs/2610.23507
AB - 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.
ER -