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A Strategy for Preparing Quantum Squeezed States Using Reinforcement Learning
April 30, 2024, 4:46 a.m. | X. L. Zhao, Y. M. Zhao, M. Li, T. T. Li, Q. Liu, S. Guo, X. X. Yi
stat.ML updates on arXiv.org arxiv.org
Abstract: 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 …
abstract application arxiv collective control dynamics engineer fields linear quant-ph quantum reinforcement reinforcement learning spin stat.ml strategy type
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