Deep learning enhanced individual nuclear-spin detection
Kyunghoon Jung (Seoul National University)
M. H. Abobeih (TU Delft - QuTech Advanced Research Centre, Kavli institute of nanoscience Delft, TU Delft - QID/Taminiau Lab)
Jiwon Yun (Seoul National University)
Gyeonghun Kim (Seoul National University)
Hyunseok Oh (Seoul National University)
Ang Henry (University College London)
T. H. Taminiau (TU Delft - QID/Taminiau Lab, Kavli institute of nanoscience Delft, TU Delft - QuTech Advanced Research Centre)
Dohun Kim (Seoul National University)
More Info
expand_more
Other than for strictly personal use, it is not permitted to download, forward or distribute the text or part of it, without the consent of the author(s) and/or copyright holder(s), unless the work is under an open content license such as Creative Commons.
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.