.png){kind=icon}
.png){kind=icon}
Return
Selection algorithm for observation points in environmental data assimilation based on the quantum squeezing effect
.png){kind=icon}
PRE
AI
DOI：10.1007/s11433-025-2703-7.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
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.
Journal
S
IF:
7.5
Papers:
3.9K
Citations:
7.4K