Bayesian parameter estimation by continuous homodyne detection

TL;DR

This paper demonstrates how Bayesian analysis of continuous homodyne detection can improve parameter estimation in quantum systems by exploiting transient dynamics and measurement noise correlations.

Contribution

It introduces a method combining Bayesian inference with continuous homodyne detection to enhance quantum parameter estimation, highlighting the role of transient dynamics and noise correlations.

Findings

Transient evolution is more sensitive to parameters than steady state.

Measurement back-action influences system dynamics and parameter sensitivity.

Temporal noise correlations impact the ultimate sensitivity limit.

Abstract

We simulate the process of continuous homodyne detection of the radiative emission from a quantum system, and we investigate how a Bayesian analysis can be employed to determine unknown parameters that govern the system evolution. Measurement back-action quenches the system dynamics at all times and we show that the ensuing transient evolution is more sensitive to system parameters than the steady state of the system. The parameter sensitivity can be quantified by the Fisher information, and we investigate numerically and analytically how the temporal noise correlations in the measurement signal contribute to the ultimate sensitivity limit of homodyne detection.