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6CCVD Research

Machine-learning based high-bandwidth magnetic sensing

At a Glance

Section titled ā€œAt a Glanceā€
MetadataDetails
Publication Date2025-06-16
JournalMachine Learning Science and Technology
AuthorsGalya Haim, Stefano Martina, John Howell, Nir Barā€Gill, Filippo Caruso

Abstract

Section titled ā€œAbstractā€

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.

Tech Support

Section titled ā€œTech Supportā€

Original Source

Section titled ā€œOriginal Sourceā€

References

Section titled ā€œReferencesā€
  1. 2012 - Theory of the ground-state spin of the NVāˆ’ center in diamond [Crossref]
  2. 2013 - Nanoscale magnetic imaging of a single electron spin under ambient conditions [Crossref]
  3. 2017 - Quantitative vectorial magnetic imaging of multi-domain rock forming minerals using nitrogen-vacancy centers in diamond [Crossref]
  4. 2021 - High-sensitivity, high-resolution detection of reactive oxygen species concentration using NV centers [Crossref]
  5. 2013 - Nanoscale nuclear magnetic resonance with a nitrogen-vacancy spin sensor [Crossref]
  6. 2008 - Nanoscale magnetic sensing with an individual electronic spin in diamond [Crossref]
  7. 2017 - Quantum machine learning [Crossref]
  8. 2022 - Learning the noise fingerprint of quantum devices [Crossref]

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