期刊
NEW JOURNAL OF PHYSICS
卷 22, 期 3, 页码 -出版社
IOP PUBLISHING LTD
DOI: 10.1088/1367-2630/ab6f1f
关键词
quantum metrology; machine learning; reinforcement learning; quantum-chaotic sensors; control theory; spin squeezing; superradiance master equation
资金
- Deutsche Forschungsgemeinschaft (DFG) [BR 5221/1-1]
- Open Access Publishing Fund of the University of Tubingen
Recently proposed quantum-chaotic sensors achieve quantum enhancements in measurement precision by applying nonlinear control pulses to the dynamics of the quantum sensor while using classical initial states that are easy to prepare. Here, we use the cross-entropy method of reinforcement learning (RL) to optimize the strength and position of control pulses. Compared to the quantum-chaotic sensors with periodic control pulses in the presence of superradiant damping, we find that decoherence can be fought even better and measurement precision can be enhanced further by optimizing the control. In some examples, we find enhancements in sensitivity by more than an order of magnitude. By visualizing the evolution of the quantum state, the mechanism exploited by the RL method is identified as a kind of spin-squeezing strategy that is adapted to the superradiant damping.
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