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arXiv · 2006.13478

Deep learning enhanced individual nuclear-spin detection

Kyunghoon Jung·M. H. Abobeih·Jiwon Yun·Gyeonghun Kim·Hyunseok Oh·Henry Ang·T. H. Taminiau·Dohun Kim
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Abstract

The detection of nuclear spins using individual electron spins has enabled new 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 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 enable efficient imaging of complex spin samples and automatic characterization of large spin-qubit registers.

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Kyunghoon Jung, M. H. Abobeih, Jiwon Yun, Gyeonghun Kim, Hyunseok Oh, Henry Ang, T. H. Taminiau, Dohun Kim. 2020-06-24. Deep learning enhanced individual nuclear-spin detection. https://doi.org/10.1038/s41534-021-00377-3

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