Machine-learning based high-bandwidth magnetic sensing
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arXiv
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| Autores principales: | , , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| _version_ | 1866908415348965376 |
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| author | Haim, Galya Martina, Stefano Howell, John Bar-Gill, Nir Caruso, Filippo |
| author_facet | Haim, Galya Martina, Stefano Howell, John Bar-Gill, Nir Caruso, Filippo |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_12820 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Machine-learning based high-bandwidth magnetic sensing Haim, Galya Martina, Stefano Howell, John Bar-Gill, Nir Caruso, Filippo Quantum Physics Artificial Intelligence Machine Learning Applied Physics Computational Physics 68T07 (Primary) 68T10, 81-08, 81-05, 81-10, 81-11, 81V10 (Secondary) I.2.6; I.5.4; J.2; I.6.3 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. |
| title | Machine-learning based high-bandwidth magnetic sensing |
| topic | Quantum Physics Artificial Intelligence Machine Learning Applied Physics Computational Physics 68T07 (Primary) 68T10, 81-08, 81-05, 81-10, 81-11, 81V10 (Secondary) I.2.6; I.5.4; J.2; I.6.3 |
| url | https://arxiv.org/abs/2409.12820 |