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arXiv · 2410.10615

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

Matt Overton·Jesús Rubio·Nathan Cooper·Daniele Baldolini·David Johnson·Janet Anders·Lucia Hackermüller
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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.

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Matt Overton, Jesús Rubio, Nathan Cooper, Daniele Baldolini, David Johnson, Janet Anders, Lucia Hackermüller. 2024-10-14. Adaptive, symmetry-informed Bayesian metrology for precise quantum technology measurements. https://doi.org/10.1103/rq95-zq9r

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