Robust Quantum Sensing with Multiparameter Decorrelation

TL;DR

This paper introduces a versatile quantum sensing method that uses multiparameter estimation and machine learning to create robust protocols, effectively reducing noise correlations and enhancing sensitivity in quantum measurements.

Contribution

It presents a novel, adaptable approach combining multiparameter estimation theory and machine learning to design noise-robust quantum sensing protocols.

Findings

Successfully decorrelated accelerometer sensitivity from lattice noise

Demonstrated improved measurement precision through Bayesian analysis

Applicable to various quantum platforms for enhanced metrology

Abstract

The performance of a quantum sensor is fundamentally limited by noise. This noise is particularly damaging when it becomes correlated with the readout of a target signal, caused by fluctuations of the sensor's operating parameters. These uncertainties limit sensitivity in a way that can be understood with multiparameter estimation theory. We develop a new approach, adaptable to any quantum platform, for designing robust sensing protocols that leverages multiparameter estimation theory and machine learning to decorrelate a target signal from fluctuating off-target (``nuisance'') parameters. Central to our approach is the identification of information-theoretic goals that guide a machine learning agent through an otherwise intractably large space of potential sensing protocols. As an illustrative example, we apply our approach to a reconfigurable optical lattice to design an accelerometer whose sensitivity is decorrelated from lattice depth noise. We demonstrate the effect of decorrelation on outcomes and Bayesian inferencing through statistical analysis in parameter space, and discuss implications for future applications in quantum metrology and computing.