Machine-learning based high-bandwidth magnetic 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-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.

12 pages including supplementary, 5 figures, 3 supplementary figures

References in corpus (12)

• Quantum Machine Learning
• High-sensitivity diamond magnetometer with nanoscale resolution
• Nanoscale magnetic imaging of a single electron spin under ambient conditions
• Theory of the ground state spin of the NV- center in diamond: II. Spin solutions, time-evolution, relaxation and inhomogeneous dephasing
• Theory of the ground state spin of the NV- center in diamond: I. Fine structure, hyperfine structure, and interactions with electric, magnetic and strain fields
• Universal Function Approximation by Deep Neural Nets with Bounded Width and ReLU Activations
• Machine-learning-enhanced quantum sensors for accurate magnetic field imaging
• Quantum-Noise-Driven Generative Diffusion Models
• Hardware Trojan Detection Using Unsupervised Deep Learning on Quantum Diamond Microscope Magnetic Field Images
• Quantitative vectorial magnetic imaging of multi domain rock forming minerals using nitrogen-vacancy centers in diamond
• Deep learning enhanced noise spectroscopy of a spin qubit environment
• Transfer learning with generative models for object detection on limited datasets