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<p>In this paper, a quantum-enhanced framework is proposed to optimize observation point selection in environmental data assimilation. The method transforms the task into a QUBO problem, balancing uncertainty reduction and spatial diversity. By leveraging a quantum-inspired optical Ising machine, it avoids the exponential complexity of classical optimization. Tests on the Lorenz-1996 model demonstrate its superiority over traditional methods, enhancing computational efficiency without loss of accuracy. The findings underscore the potential of quantum-inspired optimization for scalable, real-time assimilation in high-resolution weather prediction, reducing dimensionality and computational cost.</p>

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Selection algorithm for observation points in environmental data assimilation based on the quantum squeezing effect

  • 期刊论文

  • 发表日期：

    2025年8月11日

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  • Hanyu Yang,
  • Runqing Zhang,
  • Zhihong Zhang,
  • Nengfei Gong,
  • Yancheng Jiang,
  • Yuxuan Jia,
  • Tiejun Wang

摘要

In this paper, a quantum-enhanced framework is proposed to optimize observation point selection in environmental data assimilation. The method transforms the task into a QUBO problem, balancing uncertainty reduction and spatial diversity. By leveraging a quantum-inspired optical Ising machine, it avoids the exponential complexity of classical optimization. Tests on the Lorenz-1996 model demonstrate its superiority over traditional methods, enhancing computational efficiency without loss of accuracy. The findings underscore the potential of quantum-inspired optimization for scalable, real-time assimilation in high-resolution weather prediction, reducing dimensionality and computational cost.