基于人工神经网络的超冷原子实验多参数自主优化系统  

Multiparameter Autonomous Optimization System for Ultracold Atomic Experiments Based on Artificial Neural Network

作　　者：刘乾 谢昱 李琳[1,2] 梁昂昂 李文文 程鹤楠 方苏 屈求智 刘亮[1] 汪斌[1] 吕德胜[1] Liu Qian;Xie Yu;Li Lin;Liang Ang’ang;Li Wenwen;Chen Henan;Fang Su;Qu Qiuzhi;Liu Liang;Wang Bin;LV Desheng(Key Laboratory of Quantum Optics,Shanghai Institute of Optics and Fine Mechanics,Chinese Academy of Sciences,Shanghai 201800,China;College of Materials Science and Opto-Electronic Technology,University of Chinese Academy of Sciences,Beijing 100049,China)

机构地区：[1]中国科学院上海光学精密机械研究所量子光学重点实验室,上海201800 [2]中国科学院大学材料科学与光电技术学院,北京100049

出　　处：《中国激光》2021年第24期176-183,共8页Chinese Journal of Lasers

基　　金：国家自然科学基金(12004401);中国科学院青年创新促进会(2013YQ09094304)。

摘　　要：实验参数的优化调整在冷原子实验中是最基础且最重要的工作,优良实验结果的获得离不开实验参数的不断优化。提出了一种基于人工神经网络的超冷原子实验多参数自主优化方案。首先,详细介绍了神经网络结构的设置、自优化的学习策略。然后,将神经网络与全局最优化模拟退火算法相结合搭建了相应的多参数自主优化系统。最后,进行了利用直接蒙特卡罗模拟方法模拟磁光阱中冷原子动力学行为的实验,对所提系统的性能进行了验证。实验结果表明,经过约30 h的优化迭代,所提方案有效完成了对冷原子动力学行为实验系统输入参数的优化,并得到了一个稳定、有效且最优的实验结果。Objective The optimal adjustment of experimental parameters is the most basic and important work in cold atomic experiments.Excellent experimental results cannot be achieved without the continuous optimization of parameters.In this paper,a multiparameter autonomous optimization system for ultracold atomic experiment based on artificial neural network is proposed.The setup of neural network structure,the autonomous optimization learning approach,and the construction of a multiparameter autonomous optimization system using the global optimization simulated annealing algorithm(SAA)are described in detail.The parameter autonomous optimization system is validated by using direct simulation Monte Carlo(DSMC)method to simulate the kinetic behavior of cold atoms in magneto-optical trap(MOT).The final results reveal that after about 30hof optimization iteration,the input parameters of validation experimental system are effectively optimized,yielding a stable,efficient,and optimal experimental result.Therefore,the multiparameter autonomous optimization system based on artificial neural network can greatly reduce manual workload,enhance experimental system utilization,and improve the quality of the final experimental results.The proposed scheme provides a solution for engineering and remote control of cold atomic experimental systems.Methods This paper presents a detailed scheme for a multiparameter autonomous optimization system using artificial neural networks.The setup of the neural network structure,the autonomous optimization learning strategy,and the construction of a multiparameter autonomous optimization system using SAA are described.The experimental validation of the proposed scheme combined with the DSMC method for simulating atomic cooling experiment is shown.Finally,four parameters are employed to optimize the number of cold atoms trapped in MOT:cooling laser power,cooling laser detuning,magnetic gradient,and axial velocity of cold atomic beam.Results and Discussions For the combined simulation and deep neural

关 键 词：量子光学 激光冷却 磁光阱 人工神经网络 直接蒙特卡罗模拟 

分 类 号：O562[理学—原子与分子物理]