Improving the dynamics of quantum sensors with reinforcement learning

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 (R...

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Bibliographic Details
Published inNew journal of physics Vol. 22; no. 3; pp. 35001 - 35015
Main Authors Schuff, Jonas, Fiderer, Lukas J, Braun, Daniel
Format Journal Article
LanguageEnglish
Published Bristol IOP Publishing 01.03.2020
Subjects
control theory
Damping
Learning
machine learning
Nonlinear control
Optimization
Physics
Position sensing
quantum metrology
Quantum sensors
quantum-chaotic sensors
reinforcement learning
Sensitivity enhancement
Sensors
spin squeezing
superradiance master equation
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Summary: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.
Bibliography:NJP-111051.R1
ISSN:1367-2630
1367-2630
DOI:10.1088/1367-2630/ab6f1f