Comparison of differential evolution, particle swarm optimization,quantum-behaved particle swarm optimization, and quantum evolutionary algorithm for preparation of quantum states  

作　　者：程鑫 鲁秀娟 刘亚楠 匡森 Xin Cheng;Xiu-Juan Lu;Ya-Nan Liu;Sen Kuang(Department of Automation,University of Science and Technology of China,Hefei 230027,China;Department of Mechanical Engineering,The University of Hong Kong,Hong Kong 999077,China;Quantum Machines Unit,Okinawa Institute of Science and Technology Graduate University,Okinawa 904-0495,Japan)

机构地区：[1]Department of Automation,University of Science and Technology of China,Hefei 230027,China [2]Department of Mechanical Engineering,The University of Hong Kong,Hong Kong 999077,China [3]Quantum Machines Unit,Okinawa Institute of Science and Technology Graduate University,Okinawa 904-0495,Japan

出　　处：《Chinese Physics B》2023年第2期53-59,共7页中国物理B（英文版）

基　　金：supported by the National Natural Science Foundation of China (Grant No. 61873251)。

摘　　要：Four intelligent optimization algorithms are compared by searching for control pulses to achieve the preparation of target quantum states for closed and open quantum systems, which include differential evolution(DE), particle swarm optimization(PSO), quantum-behaved particle swarm optimization(QPSO), and quantum evolutionary algorithm(QEA).We compare their control performance and point out their differences. By sampling and learning for uncertain quantum systems, the robustness of control pulses found by these four algorithms is also demonstrated and compared. The resulting research shows that the QPSO nearly outperforms the other three algorithms for all the performance criteria considered.This conclusion provides an important reference for solving complex quantum control problems by optimization algorithms and makes the QPSO be a powerful optimization tool.

关 键 词：quantum control state preparation intelligent optimization algorithm 

分 类 号：O413[理学—理论物理] TP18[理学—物理]