Machine-learning based high-bandwidth magnetic sensing

TL;DR

This paper introduces machine learning techniques to improve nitrogen-vacancy diamond-based magnetic sensing, significantly enhancing bandwidth and sensitivity tradeoffs and reducing data requirements for quantum sensing applications.

Contribution

It presents a novel application of machine learning to optimize NV quantum sensing, addressing sensitivity, dynamic range, and bandwidth tradeoffs.

Findings

Data points reduced by at least a factor of 3

Maintains current error levels with fewer data

Enhances feasibility of quantum sensing technologies

Abstract

Recent years have seen significant growth of quantum technologies, and specifically quantum sensing, both in terms of the capabilities of advanced platforms and their applications. One of the leading platforms in this context is nitrogen-vacancy (NV) color centers in diamond, providing versatile, high-sensitivity, and high-spatial-resolution magnetic sensing. Nevertheless, current schemes for spin resonance magnetic sensing (as applied by NV quantum sensing) suffer from tradeoffs associated with sensitivity, dynamic range, and bandwidth. Here we address this issue, and implement machine learning tools to enhance NV magnetic sensing in terms of the sensitivity/bandwidth tradeoff in large dynamic range scenarios. Our results indicate a potential reduction of required data points by at least a factor of 3, while maintaining the current error level. Our results promote quantum machine learning protocols for sensing applications towards more feasible and efficient quantum technologies.