A Strategy for Preparing Quantum Squeezed States Using Reinforcement Learning
arxiv(2024)
摘要
We propose a scheme leveraging reinforcement learning to engineer control
fields for generating non-classical states. It is exemplified by the
application to prepare spin-squeezed states for an open collective spin model
where a linear control field is designed to govern the dynamics. The
reinforcement learning agent determines the temporal sequence of control
pulses, commencing from a coherent spin state in an environment characterized
by dissipation and dephasing. Compared to the constant control scenario, this
approach provides various control sequences maintaining collective spin
squeezing and entanglement. It is observed that denser application of the
control pulses enhances the performanceof the outcomes. However, there is a
minor enhancement in the performance by adding control actions. The proposed
strategy demonstrates increased effectiveness for larger systems. Thermal
excitations of the reservoir are detrimental to the control outcomes. Feasible
experiments are suggested to implement the control proposal. The extension to
continuous control problems and another quantum system are discussed. The
replaceability of the reinforcement learning module is also emphasized. This
research paves the way for its application in manipulating other quantum
systems.
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