Resource-efficient adaptive Bayesian tracking of magnetic fields with a quantum sensor

TL;DR

This paper introduces a fast, approximate Bayesian method using Gaussian sums for real-time magnetic field tracking with quantum sensors, significantly reducing computation time while maintaining accuracy.

Contribution

It proposes an efficient Gaussian sum approximation for Bayesian estimation, enabling real-time adaptive magnetic field tracking with quantum sensors.

Findings

Reduction in computation time by a factor of 10 compared to existing methods

Outperforms previous approaches in tracking accuracy in certain regimes

Effective for T2* = 1 microsecond despite small time savings

Abstract

Single-spin quantum sensors, for example based on nitrogen-vacancy centres in diamond, provide nanoscale mapping of magnetic fields. In applications where the magnetic field may be changing rapidly, total sensing time is crucial and must be minimised. Bayesian estimation and adaptive experiment optimisation can speed up the sensing process by reducing the number of measurements required. These protocols consist of computing and updating the probability distribution of the magnetic field based on measurement outcomes and of determining optimized acquisition settings for the next measurement. However, the computational steps feeding into the measurement settings of the next iteration must be performed quickly enough to allow for real-time updates. This article addresses the issue of computational speed by implementing an approximate Bayesian estimation technique, where probability distributions are approximated by a finite sum of Gaussian functions. Given that only three parameters are required to fully describe a Gaussian density, we find that in many cases, the magnetic field probability distribution can be described by fewer than ten parameters, achieving a reduction in computation time by factor 10 compared to existing approaches. For T2* = 1 micro second, only a small decrease in computation time is achieved. However, in these regimes, the proposed Gaussian protocol outperforms the existing one in tracking accuracy.