论文标题

使用高斯州和测量结果估算贝叶斯参数

Bayesian parameter estimation using Gaussian states and measurements

论文作者

Morelli, Simon, Usui, Ayaka, Agudelo, Elizabeth, Friis, Nicolai

论文摘要

贝叶斯分析是一个参数估计的框架,即使在不确定性方案中也适用于基于Cramér-Rao结合的常用局部(频繁)分析,也无法很好地定义。特别是,当没有有关参数值的初始信息时,例如,当执行几乎没有测量时,它就会适用。在这里,我们考虑了连续变量量子计量学(位移,阶段和挤压强度的估计)中的三个范式估计方案,并从贝叶斯的角度分析它们。对于每种情况,我们都会研究在同性恋和异差检测下使用单模高斯状态实现的精确度。这使我们能够确定贝叶斯估计策略,这些策略将良好的性能与在高斯州和测量方面的直接实验实现相结合。我们的结果提供了实用的解决方案,以达到适用当地估计技术的不确定性,从而将差距弥合到可以采用渐近最佳策略的制度。

Bayesian analysis is a framework for parameter estimation that applies even in uncertainty regimes where the commonly used local (frequentist) analysis based on the Cramér-Rao bound is not well defined. In particular, it applies when no initial information about the parameter value is available, e.g., when few measurements are performed. Here, we consider three paradigmatic estimation schemes in continuous-variable quantum metrology (estimation of displacements, phases, and squeezing strengths) and analyse them from the Bayesian perspective. For each of these scenarios, we investigate the precision achievable with single-mode Gaussian states under homodyne and heterodyne detection. This allows us to identify Bayesian estimation strategies that combine good performance with the potential for straightforward experimental realization in terms of Gaussian states and measurements. Our results provide practical solutions for reaching uncertainties where local estimation techniques apply, thus bridging the gap to regimes where asymptotically optimal strategies can be employed.

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