Active Learning Approach to Optimization of Experimental Control
Yadong Wu1 , Zengming Meng2 , Kai Wen2 , Chengdong Mi2 , Jing Zhang2* , and Hui Zhai1*
1 Institute for Advanced Study, Tsinghua University, Beijing 100084, China2 State Key Laboratory of Quantum Optics and Quantum Optics Devices, and Institute of Opto-Electronics, Collaborative Innovation Center of Extreme Optics, Shanxi University, Taiyuan 030006, China
Abstract ：We present a general machine learning based scheme to optimize experimental control. The method utilizes the neural network to learn the relation between the control parameters and the control goal, with which the optimal control parameters can be obtained. The main challenge of this approach is that the labeled data obtained from experiments are not abundant. The central idea of our scheme is to use the active learning to overcome this difficulty. As a demonstration example, we apply our method to control evaporative cooling experiments in cold atoms. We have first tested our method with simulated data and then applied our method to real experiments. It is demonstrated that our method can successfully reach the best performance within hundreds of experimental runs. Our method does not require knowledge of the experimental system as a prior and is universal for experimental control in different systems.
收稿日期: 2020-07-28
出版日期: 2020-09-29
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2020, Vol. 37 
