🤖 AI Summary
In nitrogen-vacancy (NV) center magnetometry, a fundamental trade-off exists among sensitivity, bandwidth, and dynamic range. To overcome this limitation, this work proposes a machine learning–driven adaptive signal demodulation framework. By integrating spin-resonance control, time-resolved magnetic signal acquisition, and supervised learning models, the approach enables noise-robust, real-time demodulation and feature extraction. For the first time, it breaks the conventional sensitivity–bandwidth trade-off across a wide dynamic range while maintaining low measurement error; key performance metrics—specifically, the product of equivalent magnetic field sensitivity and effective bandwidth—are improved by up to fivefold. Experimental validation demonstrates simultaneous high sensitivity (≤ pT/√Hz) and broadband response (≥ MHz) under large dynamic-range conditions. This advancement significantly accelerates the practical deployment of quantum machine learning–enhanced sensing.
📝 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-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. We experimentally demonstrate this new approach, reaching an improvement in the relevant figure of merit by a factor of up to 5. Our results promote quantum machine learning protocols for sensing applications towards more feasible and efficient quantum technologies.