Artificial intelligence enhanced two-dimensional nanoscale nuclear magnetic resonance spectroscopy

Two-dimensional nuclear magnetic resonance (NMR) is indispensable to molecule structure determination. Nitrogen-vacancy center in diamond has been proposed and developed as an outstanding quantum sensor to realize NMR in nanoscale or even single molecule. However, like conventional multi-dimensional...

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Bibliographic Details
Published in:npj quantum information 2020-09, Vol.6 (1), Article 79
Main Authors: Kong, Xi, Zhou, Leixin, Li, Zhijie, Yang, Zhiping, Qiu, Bensheng, Wu, Xiaodong, Shi, Fazhan, Du, Jiangfeng
Format: Article
Language:English
Subjects:
639/705/1042
639/766/483/1255
Artificial intelligence
Classical and Quantum Gravitation
Environmental effects
Physics
Physics and Astronomy
Quantum Computing
Quantum Field Theories
Quantum Information Technology
Quantum Physics
Quantum theory
Relativity Theory
Spintronics
String Theory
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Summary:Two-dimensional nuclear magnetic resonance (NMR) is indispensable to molecule structure determination. Nitrogen-vacancy center in diamond has been proposed and developed as an outstanding quantum sensor to realize NMR in nanoscale or even single molecule. However, like conventional multi-dimensional NMR, a more efficient data accumulation and processing method is necessary to realize applicable two-dimensional (2D) nanoscale NMR with a high spatial resolution nitrogen-vacancy sensor. Deep learning is an artificial algorithm, which mimics the network of neurons of human brain, has been demonstrated superb capability in pattern identifying and noise canceling. Here we report a method, combining deep learning and sparse matrix completion, to speed up 2D nanoscale NMR spectroscopy. The signal-to-noise ratio is enhanced by 5.7 ± 1.3 dB in 10% sampling coverage by an artificial intelligence protocol on 2D nanoscale NMR of a single nuclear spin cluster. The artificial intelligence algorithm enhanced 2D nanoscale NMR protocol intrinsically suppresses the observation noise and thus improves sensitivity.
ISSN:2056-6387
2056-6387
DOI:10.1038/s41534-020-00311-z