A Strategy for Preparing Quantum Squeezed States Using Reinforcement Learning

arXiv:2401.16320v2 Announce Type: replace-cross
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