Machine-learning based high-bandwidth magnetic sensing

Year: 2025

Authors: Haim G., Martina S., Howell J., Bar-Gill N., Caruso F.

Autors Affiliation: Hebrew Univ Jerusalem, Inst Appl Phys, IL-91904 Jerusalem, Israel; Univ Melbourne, Sch Phys, Parkville, Vic 3010, Australia; Univ Florence, Dept Phys & Astron, via Sansone 1, I-50019 Sesto Fiorentino, FI, Italy; Univ Florence, European Lab Nonlinear Spect LENS, via N Carrara 1, I-50019 Sesto Fiorentino, FI, Italy; Hebrew Univ Jerusalem, Racah Inst Phys, IL-91904 Jerusalem, Israel; CNR, Ist Nazl Ott, I-50019 Sesto Fiorentino, FI, Italy.

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.

Journal/Review: MACHINE LEARNING-SCIENCE AND TECHNOLOGY

Volume: 6 (2)      Pages from: 25074-1  to: 25074-11

More Information: This work was financially supported by the European Union’s Horizon 2020 research and innovation programme under FET-OPEN GA No. 828946-PATHOS. G H also acknowledges support from the Melbourne research scholarship. S M also acknowledges financial support from the PNRR MUR Project PE0000023-NQSTI. N B and F C also acknowledge financial support by the European Commission’s Horizon Europe Framework Programme under the Research and Innovation Action GA No. 101070 546-MUQUABIS. N B also acknowledges financial support by the Carl Zeiss Stiftung (HYMMS wildcard), the Ministry of Science and Technology, Israel, the innovation authority (Project No. 70033), and the ISF (Grants No. 1380/21 and 3597/21). F C also acknowledges financial support by the European Defence Agency under the Project Q-LAMPS Contract No. B PRJ-RT-989.
KeyWords: quantum sensing; magnetic sensing; machine learning; quantum machine learning; nitrogen vacancy (NV) centers; neural networks
DOI: 10.1088/2632-2153/ade51c