Optimizing quantum-enhanced Bayesian multiparameter estimation of phase and noise in practical sensors

TL;DR

This paper develops an optimized Bayesian multiparameter estimation method for quantum sensors that accounts for noise and resource limitations, enabling sensors to surpass the standard quantum limit in practical scenarios.

Contribution

It introduces a novel optimization approach for multiparameter quantum estimation in noisy environments, enhancing practical sensor performance beyond the standard quantum limit.

Findings

Optimization improves estimation accuracy in noisy quantum sensors

Selective parameter focus enhances resource utilization

Method enables sensors to outperform standard quantum limit

Abstract

Achieving quantum-enhanced performances when measuring unknown quantities requires developing suitable methodologies for practical scenarios, that include noise and the availability of a limited amount of resources. Here, we report on the optimization of sub-standard quantum limit Bayesian multiparameter estimation in a scenario where a subset of the parameters describes unavoidable noise processes in an experimental photonic sensor. We explore how the optimization of the estimation changes depending on which parameters are either of interest or are treated as nuisance ones. Our results show that optimizing the multiparameter approach in noisy apparata represents a significant tool to fully exploit the potential of practical sensors operating beyond the standard quantum limit for broad resources range.