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Recently proposed quantum-chaotic sensors achieve quantum enhancements inmeasurement precision by applying nonlinear control pulses to the dynamics ofthe quantum sensor while using classical initial states that are easy toprepare. Here, we use the cross-entropy method of reinforcement learning tooptimize the strength and position of control pulses. Compared to thequantum-chaotic sensors with periodic control pulses in the presence ofsuperradiant damping, we find that decoherence can be fought even better andmeasurement precision can be enhanced further by optimizing the control. Insome examples, we find enhancements in sensitivity by more than an order ofmagnitude. By visualizing the evolution of the quantum state, the mechanismexploited by the reinforcement learning method is identified as a kind ofspin-squeezing strategy that is adapted to the superradiant damping.

Improving the dynamics of quantum sensors with reinforcement learning