用贝叶斯似然自由方法和深度学习解锁量子传感的光探测
Unlocking photodetection for quantum sensing with Bayesian likelihood-free methods and deep learning
AI总结：
本文提出利用贝叶斯似然自由方法和深度学习提升量子传感中光探测的实时参数估计能力，通过实验验证了在非经典光子统计特征下的有效性。
摘要：
为了在实时条件下使量子传感器达到量子极限，必须识别高效的数据显示推断工具以实现快速参数估计。在光探测中，关键挑战是快速解释具有非经典统计特征的点击模式——这些特征正是导致量子精度提升的原因。我们通过比较贝叶斯似然自由方法与基于深度学习（DL）的方法来实现这一目标。虽然前者更具概念直观性，但后者一旦训练好，可以提供显著更快的估计，精度相当，并能产生相似的误差预测，挑战了深度学习缺乏此类能力的普遍误解。我们首先验证了两种方法在可解析但多参数的两能级系统发射不相关光子的场景中的有效性。然而，我们的主要结果是将其应用于驱动非线性光学机械装置，该装置发射具有复杂多点击相关性的非经典光；在这种情况下，我们的方法对于快速推断至关重要，因此解锁了在实时条件下区分不同光子统计特征的可能性。我们的结果为利用光探测中的非经典效应动态控制量子传感器铺平了道路。
英文摘要：
To operate quantum sensors at their quantum limit in real time, it is crucial to identify efficient data inference tools for rapid parameter estimation. In photodetection, the key challenge is the fast interpretation of click-patterns that exhibit non-classical statistics-the very features responsible for the quantum enhancement of precision. We achieve this goal by comparing Bayesian likelihood-free methods with ones based on deep learning (DL). While the former are more conceptually intuitive, the latter, once trained, provide significantly faster estimates with comparable precision and yield similar predictions of the associated errors, challenging a common misconception that DL lacks such capabilities. We first verify both approaches for an analytically tractable, yet multiparameter, scenario of a two-level system emitting uncorrelated photons. Our main result, however, is the application to a driven non-linear optomechanical device emitting non-classical light with complex multiclick correlations. Although both methods facilitate inference, only DL provides the execution speed required for real-time performance while naturally learning complex multi-photon click-patterns. Our results pave the way for real-time sensing and dynamical control of quantum devices that leverage non-classical effects in photodetection.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
