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<p>Data assimilation (DA), vital for numerical weather prediction and Earth system modeling, struggles with escalating computational demands from complex environmental datasets. We introduce a quantum-inspired DA (QDA) framework using coherent Ising machines (CIMs): an optical solver based on the quantum squeezing effect, specialized in solving combinatorial optimization problems. The QDA method overcomes qubit limitations with domain decomposition and reformulates a classical three-dimensional variational assimilation (3D-VAR) into a quadratic unconstrained binary optimization (QUBO) problem mapped to an optical Ising Hamiltonian. Validated on a 512D quasi-geostrophic model, numerically simulated QDA achieves lower root mean square error than the classical method after 120 assimilation cycles. Single-assimilation cycle optical experiments show that QDA operates at 9.5% (25.31 ms) of the classical 3D-VAR runtime (266.4 ms), yielding a 10.5× speedup while maintaining accuracy. This demonstrates the potential of CIMs to revolutionize high-resolution environmental forecasting through energy-efficient, real-time assimilation, bridging quantum photonics and geophysics.</p>

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Realization of quantum data assimilation for a two-dimensional quasi-geostrophic model based on a coherent Ising machine

  • 期刊论文

  • 发表日期：

    2026年3月26日

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  • Yu-Xuan Jia,
  • Wei Zhong,
  • Wei You,
  • Hua-Dong Du,
  • Neng-Fei Gong,
  • Yan-Chen Jiang,
  • Yao Yao,
  • Yi-Xing Cao,
  • Li-Xin Yuan,
  • Yin Ma,
  • Kai Wen,
  • Tie-Jun Wang

摘要

Data assimilation (DA), vital for numerical weather prediction and Earth system modeling, struggles with escalating computational demands from complex environmental datasets. We introduce a quantum-inspired DA (QDA) framework using coherent Ising machines (CIMs): an optical solver based on the quantum squeezing effect, specialized in solving combinatorial optimization problems. The QDA method overcomes qubit limitations with domain decomposition and reformulates a classical three-dimensional variational assimilation (3D-VAR) into a quadratic unconstrained binary optimization (QUBO) problem mapped to an optical Ising Hamiltonian. Validated on a 512D quasi-geostrophic model, numerically simulated QDA achieves lower root mean square error than the classical method after 120 assimilation cycles. Single-assimilation cycle optical experiments show that QDA operates at 9.5% (25.31 ms) of the classical 3D-VAR runtime (266.4 ms), yielding a 10.5× speedup while maintaining accuracy. This demonstrates the potential of CIMs to revolutionize high-resolution environmental forecasting through energy-efficient, real-time assimilation, bridging quantum photonics and geophysics.