Detection of entangled states supported by reinforcement learning
Abstract
Discrimination of entangled states is an important element of quantum enhanced metrology. This typically requires low-noise detection technology. Such a challenge can be circumvented by introducing nonlinear readout process. Traditionally, this is realized by reversing the very dynamics that generates the entangled state, which requires a full control over the system evolution. In this work, we present nonlinear readout of highly entangled states by employing reinforcement learning (RL) to manipulate the spin-mixing dynamics in a spin-1 atomic condensate. The RL found results in driving the system towards an unstable fixed point, whereby the (to be sensed) phase perturbation is amplified by the subsequent spin-mixing dynamics. Working with a condensate of 10900 {87}^Rb atoms, we achieve a metrological gain of 6.97 dB beyond the classical precision limit. Our work would open up new possibilities in unlocking the full potential of entanglement caused quantum enhancement in experiments.
Keywords
Cite
@article{arxiv.2307.09176,
title = {Detection of entangled states supported by reinforcement learning},
author = {Jia-Hao Cao and Feng Chen and Qi Liu and Tian-Wei Mao and Wen-Xin Xu and Ling-Na Wu and Li You},
journal= {arXiv preprint arXiv:2307.09176},
year = {2023}
}