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Analyzing nuclear spins in diamond at room temperature based on traversal periodic-signal identification

MetadataDetails
Publication Date2022-06-27
JournalApplied Physics Letters
AuthorsFeifei Zhou, Xueying Zhou, Zhiyi Hu, Yumeng Song, Ting Zhang
InstitutionsHefei University of Technology, Zhejiang Lab
Citations1

Detecting and imaging nuclear spins are of fundamental importance for spin-based quantum information processing in diamond. It is often realized by means of dynamical decoupling (DD) strategies, where a high-efficiency method for DD spectral analysis is required. Previously, a deep-learning-based algorithm is developed and applied on a cryogenic nitrogen-vacancy center experiment. Here, we improve the method by using a traversal periodic-signal identification approach before the deep-learning processing. With this improvement, low-resolution DD spectra with overlapped peaks could be well separated. This enables the deep-learning procedure being generally used in room-temperature experiments, where the measured spectra are often broadened with temperature. We apply this improved method in experiment, and its produced results match well with expectation. This method promises a wide range of applications in other spin-based systems.

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