论文标题
贝叶斯量子多体磁力测定法的神经网络
Neural networks for Bayesian quantum many-body magnetometry
论文作者
论文摘要
纠缠的量子多体系统可以用作传感器，可以估算具有比单个量子检测器的集合可以实现的参数的估计。通常，参数估计策略需要量子多体系统的显微镜建模，以及对其动力学的准确描述。这需要一种复杂性，可以阻碍贝叶斯推理技术的适用性。在这项工作中，我们展示了如何通过使用忠实地重现量子多体传感器动态的神经网络来规避这些问题，从而允许进行有效的贝叶斯分析。我们用由磁场驱动的XXZ模型来体现的，并表明我们的方法能够产生超出标准量子限制的磁场参数的估计。我们的工作为实际使用量子多体系统作为利用量子资源以提高精确估计的黑框传感器铺平了道路。
Entangled quantum many-body systems can be used as sensors that enable the estimation of parameters with a precision larger than that achievable with ensembles of individual quantum detectors. Typically, the parameter estimation strategy requires the microscopic modelling of the quantum many-body system, as well as a an accurate description of its dynamics. This entails a complexity that can hinder the applicability of Bayesian inference techniques. In this work we show how to circumvent these issues by using neural networks that faithfully reproduce the dynamics of quantum many-body sensors, thus allowing for an efficient Bayesian analysis. We exemplify with an XXZ model driven by magnetic fields, and show that our method is capable to yield an estimation of field parameters beyond the standard quantum limit scaling. Our work paves the way for the practical use of quantum many-body systems as black-box sensors exploiting quantum resources to improve precision estimation.
