Unlocking photodetection for quantum sensing with Bayesian likelihood-free methods and deep learning

TL;DR

This paper compares Bayesian likelihood-free methods and deep learning for rapid, real-time interpretation of photodetection data in quantum sensors, demonstrating their effectiveness in complex non-classical light scenarios.

Contribution

It introduces and validates deep learning and Bayesian likelihood-free inference methods for fast, accurate parameter estimation in quantum photodetection, including complex non-classical states.

Findings

Deep learning provides faster estimates with comparable accuracy to Bayesian methods.

Both methods successfully interpret non-classical photon statistics in real time.

Application to complex optomechanical systems demonstrates practical utility.

Abstract

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 nonlinear optomechanical device emitting non-classical light with complex multiclick correlations; in this case, our methods are essential for fast inference and, hence, unlock the possibility of distinguishing different photon statistics in real time. Our results pave the way for dynamical control of quantum sensors that leverage non-classical effects in photodetection.