Weighing unequal parameter importance and measurement expense in adaptive quantum sensing

Published

February 21, 2025

Author(s)

Michele Kelley, Robert McMichael

Abstract

A large class of experiments consists of measuring the parameters of physical models. In these experiments, the goal is to learn about these parameters as accurately and, often, quickly as possible. Adaptive experiment design works by yielding instrument control to Bayesian-based algorithms that alter instrument settings based on potential information gain about the parameters. By actively learning from data in real-time where to measure instead of determining instrument settings \empha priori}, striking improvements in experiment efficiency are possible. Here two new algorithms that improve upon previous implementations of adaptive experiment design are introduced. The first algorithm focuses learning on the model parameters that matter the most. The second algorithm considers the expense of a measurement and prioritizes information that can be gained at a lower economic cost. We demonstrate the remarkable improvement in efficiency and sensitivity that these algorithms provide for quantum sensing with nitrogen-vacancy centers in diamond. Most notably we find an almost five-fold improvement in magnetic field sensitivity.

Citation

Journal of Applied Physics

Volume

137

Issue

7

Pub Type

Journals

Download Paper

https://doi.org/10.1063/5.0251881

Local Download

Citation

Kelley, M. and McMichael, R. (2025), Weighing unequal parameter importance and measurement expense in adaptive quantum sensing, Journal of Applied Physics, [online], https://doi.org/10.1063/5.0251881, https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=959097 (Accessed April 25, 2025)