Learning Feedback Control Strategies for Quantum Metrology
نویسندگان
چکیده
We consider the problem of frequency estimation for a single bosonic field evolving under squeezing Hamiltonian and continuously monitored via homodyne detection. In particular, we exploit reinforcement learning techniques to devise feedback control strategies achieving increased precision. show that determined by neural network greatly surpasses in long-time limit performances both "no-control" strategy standard "open-loop control" strategy, which considered as benchmarks. indeed observe how devised is able optimize nontrivial preparing large fraction trajectories corresponding more sensitive quantum conditional states.
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ژورنال
عنوان ژورنال: PRX quantum
سال: 2022
ISSN: ['2691-3399']
DOI: https://doi.org/10.1103/prxquantum.3.020310