mlsanityData to 7 Oct 2026

Machine-learning based high-bandwidth magnetic sensing

arXiv 2409.12820 ↗First posted 19 Sep 2024quant-ph cs.AI cs.LGcited by 1

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Abstract (arXiv)

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.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 19 of its 45 references that are in the map. First posted on arXiv 19 Sep 2024.

Builds on

19 of 45 references.

References · 19

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7 more
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