Deep learning enhanced individual nuclear-spin detection

Abstract The detection of nuclear spins using individual electron spins has enabled diverse opportunities in quantum sensing and quantum information processing. Proof-of-principle experiments have demonstrated atomic-scale imaging of nuclear-spin samples and controlled multi-qubit registers. However...

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Autores principales:	Kyunghoon Jung, M. H. Abobeih, Jiwon Yun, Gyeonghun Kim, Hyunseok Oh, Ang Henry, T. H. Taminiau, Dohun Kim
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Physics QC1-999 Electronic computers. Computer science QA75.5-76.95
Acceso en línea:	https://doaj.org/article/aae3cb9c096d4172ab8a755f13be46a2
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spelling	oai:doaj.org-article:aae3cb9c096d4172ab8a755f13be46a22021-12-02T14:28:20ZDeep learning enhanced individual nuclear-spin detection10.1038/s41534-021-00377-32056-6387https://doaj.org/article/aae3cb9c096d4172ab8a755f13be46a22021-02-01T00:00:00Zhttps://doi.org/10.1038/s41534-021-00377-3https://doaj.org/toc/2056-6387Abstract The detection of nuclear spins using individual electron spins has enabled diverse opportunities in quantum sensing and quantum information processing. Proof-of-principle experiments have demonstrated atomic-scale imaging of nuclear-spin samples and controlled multi-qubit registers. However, to image more complex samples and to realize larger-scale quantum processors, computerized methods that efficiently and automatically characterize spin systems are required. Here, we realize a deep learning model for automatic identification of nuclear spins using the electron spin of single nitrogen-vacancy (NV) centers in diamond as a sensor. Based on neural network algorithms, we develop noise recovery procedures and training sequences for highly non-linear spectra. We apply these methods to experimentally demonstrate the fast identification of 31 nuclear spins around a single NV center and accurately determine the hyperfine parameters. Our methods can be extended to larger spin systems and are applicable to a wide range of electron-nuclear interaction strengths. These results pave the way towards efficient imaging of complex spin samples and automatic characterization of large spin-qubit registers.Kyunghoon JungM. H. AbobeihJiwon YunGyeonghun KimHyunseok OhAng HenryT. H. TaminiauDohun KimNature PortfolioarticlePhysicsQC1-999Electronic computers. Computer scienceQA75.5-76.95ENnpj Quantum Information, Vol 7, Iss 1, Pp 1-9 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Physics QC1-999 Electronic computers. Computer science QA75.5-76.95
spellingShingle	Physics QC1-999 Electronic computers. Computer science QA75.5-76.95 Kyunghoon Jung M. H. Abobeih Jiwon Yun Gyeonghun Kim Hyunseok Oh Ang Henry T. H. Taminiau Dohun Kim Deep learning enhanced individual nuclear-spin detection
description	Abstract The detection of nuclear spins using individual electron spins has enabled diverse opportunities in quantum sensing and quantum information processing. Proof-of-principle experiments have demonstrated atomic-scale imaging of nuclear-spin samples and controlled multi-qubit registers. However, to image more complex samples and to realize larger-scale quantum processors, computerized methods that efficiently and automatically characterize spin systems are required. Here, we realize a deep learning model for automatic identification of nuclear spins using the electron spin of single nitrogen-vacancy (NV) centers in diamond as a sensor. Based on neural network algorithms, we develop noise recovery procedures and training sequences for highly non-linear spectra. We apply these methods to experimentally demonstrate the fast identification of 31 nuclear spins around a single NV center and accurately determine the hyperfine parameters. Our methods can be extended to larger spin systems and are applicable to a wide range of electron-nuclear interaction strengths. These results pave the way towards efficient imaging of complex spin samples and automatic characterization of large spin-qubit registers.
format	article
author	Kyunghoon Jung M. H. Abobeih Jiwon Yun Gyeonghun Kim Hyunseok Oh Ang Henry T. H. Taminiau Dohun Kim
author_facet	Kyunghoon Jung M. H. Abobeih Jiwon Yun Gyeonghun Kim Hyunseok Oh Ang Henry T. H. Taminiau Dohun Kim
author_sort	Kyunghoon Jung
title	Deep learning enhanced individual nuclear-spin detection
title_short	Deep learning enhanced individual nuclear-spin detection
title_full	Deep learning enhanced individual nuclear-spin detection
title_fullStr	Deep learning enhanced individual nuclear-spin detection
title_full_unstemmed	Deep learning enhanced individual nuclear-spin detection
title_sort	deep learning enhanced individual nuclear-spin detection
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/aae3cb9c096d4172ab8a755f13be46a2
work_keys_str_mv	AT kyunghoonjung deeplearningenhancedindividualnuclearspindetection AT mhabobeih deeplearningenhancedindividualnuclearspindetection AT jiwonyun deeplearningenhancedindividualnuclearspindetection AT gyeonghunkim deeplearningenhancedindividualnuclearspindetection AT hyunseokoh deeplearningenhancedindividualnuclearspindetection AT anghenry deeplearningenhancedindividualnuclearspindetection AT thtaminiau deeplearningenhancedindividualnuclearspindetection AT dohunkim deeplearningenhancedindividualnuclearspindetection
_version_	1718391272534704128

Deep learning enhanced individual nuclear-spin detection

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