基于粒子群算法的量子测量系统最优参数求解

Fast Search of Quantum Measurement System Parameters via Particle Swarm Optimization

基于里德堡原子的量子测量系统相对传统测量方法具有颠覆性优势,是量子探测领域重要发展方向。然而量子测量系统要对多个实验参数进行优化以提升性能,在理论和实验上采用全空间参数扫描的直接优化方式通常比较困难。通过建立量子测量系统信号强度理论模型,并将粒子群算法应用到模型参数优化中,可快速求解测量系统全局最优参数,计算复杂度相比于传统方法降低1个数量级以上。粒子群算法原则上能应用到其他任何量子测量系统中,指导系统快速提升性能,推进实用化进程。

The quantum measurement system based on Rydberg atoms has subversive advantages over traditional measurement methods and is an important development direction in the field of quantum detection.However,quantum measurement systems need to optimize multiple experimental parameters to improve performance.It is usually difficult to use the direct optimization method of full-space parameter scanning in theory and experiment.By building a theoretical model of the quantum measurement system and applying the particle swarm optimization algorithm to the parameter optimization of the model,this paper can quickly solve the global optimal parameters of the system.The computational complexity is reduced by more than one order of magnitude compared with that of the traditional method.In principle,the particle swarm algorithm can be applied to any other quantum measurement system to guide the system to quickly improve performance and promote the practical process.