Improving the dynamics of quantum sensors with reinforcement learning
This work addresses the challenge of decoherence in quantum sensors, offering a method to enhance measurement precision for applications in quantum metrology, though it appears incremental as it builds on existing quantum-chaotic sensor frameworks.
The paper tackled the problem of improving quantum sensor precision by optimizing control pulses using reinforcement learning, achieving sensitivity enhancements of more than an order of magnitude in some cases.
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 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 reinforcement learning method is identified as a kind of spin-squeezing strategy that is adapted to the superradiant damping.