Adaptive, symmetry-informed Bayesian metrology for precise quantum technology measurements

TL;DR

This paper introduces a Bayesian, symmetry-informed adaptive strategy for quantum parameter estimation that significantly improves measurement precision and efficiency in quantum sensing experiments.

Contribution

It develops a systematic, symmetry-aware Bayesian optimization method for quantum measurements, demonstrated with a five-fold precision improvement in a cold atom experiment.

Findings

Achieved a five-fold reduction in fractional variance of the estimated parameter.

Reduced data points needed for target precision to one-third of standard procedures.

Provided general optimal estimators applicable to any parameter.

Abstract

High precision measurements are essential to solve major scientific and technological challenges, from gravitational wave detection to healthcare diagnostics. Quantum sensing delivers greater precision, but an in-depth optimisation of measurement procedures has been overlooked. Here we present a systematic strategy for parameter estimation in the low-data limit that integrates experimental control parameters and natural symmetries. The method is guided by a Bayesian quantifier of precision gain, enabling adaptive optimisation tailored to the experiment. We provide general expressions for optimal estimators for any parameter. The strategy's power is demonstrated in a quantum technology experiment, in which ultracold caesium atoms are confined in a micromachined hole in an optical fibre. We find a five-fold reduction in the fractional variance of the estimated parameter, compared to the standard measurement procedure. Equivalently, our strategy achieves a target precision with a third of the data points previously required. Such enhanced device performance and accelerated data collection will be essential for applications in quantum computing, communication, metrology, and the wider quantum technology sector.