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arXiv · 2610.23507

Solving Large-Scale Parameter Inference in Controllable Nuclear Fusion via Coherent Ising Machines and Block Coordinate Descent

Zijie Liu·Wenchao Liu·Wenxin Li·Anzhe Jiang·Hongdong Zhu·Qi Gao·Yin Ma·Hai Wei·Yao Huang·Yuehang Wang·Kai Wen·Jiangang Li
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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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Zijie Liu, Wenchao Liu, Wenxin Li, Anzhe Jiang, Hongdong Zhu, Qi Gao, Yin Ma, Hai Wei, Yao Huang, Yuehang Wang, Kai Wen, Jiangang Li. 2026-10-16. Solving Large-Scale Parameter Inference in Controllable Nuclear Fusion via Coherent Ising Machines and Block Coordinate Descent. https://arxiv.org/abs/2610.23507

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