Structural Analysis of Nuclear Spin Clusters via 2D Nanoscale Nuclear Magnetic Resonance Spectroscopy

Yang, Zhiping; Kong, Xinggong; Li, Zhijie; Yang, Kai; Yu, Pei; Wang, Pengfei; Wang, Ya; Qin, Xi; Rong, Xing; Duan, Chang‐Kui; Shi, Fazhan; Du, Jiangfeng

doi:10.1002/qute.201900136

Cited by 9 publications

(7 citation statements)

References 37 publications

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“…The nanoscale 2D NMR spectroscopy is performed on a coupled nuclear cluster probed by quantum NV sensor 7 . The system is controlled by the COSY protocol ( Fig.…”

Section: Experiments With Ai Enhancementmentioning

confidence: 99%

“…The data is adopted from ref. 7 . d The spectrum reconstructed by DLMC and MC method from 40% sampled data.…”

Section: Figmentioning

confidence: 99%

“…Single spin sensitivity NMR 3 , single-molecule magnetic resonance 4,5 , and microscopic 2D NMR 6 have been realized with NV centers. While these works on nanoscale NMR make it possible to provide an insight into molecule structure, 2D nanoscale NMR 7,8 is a crucial step. Although 2D NMR reveals much more information of the spectrum, the measuring times of the two-dimensional NMR increase quadratically with sampling numbers.…”

Section: Introductionmentioning

confidence: 99%

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Artificial intelligence enhanced two-dimensional nanoscale nuclear magnetic resonance spectroscopy

Kong

Zhou

et al. 2020

npj Quantum Inf

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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.

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“…The nanoscale 2D NMR spectroscopy is performed on a coupled nuclear cluster probed by quantum NV sensor 7 . The system is controlled by the COSY protocol ( Fig.…”

Section: Experiments With Ai Enhancementmentioning

confidence: 99%

“…The data is adopted from ref. 7 . d The spectrum reconstructed by DLMC and MC method from 40% sampled data.…”

Section: Figmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Artificial intelligence enhanced two-dimensional nanoscale nuclear magnetic resonance spectroscopy

Kong

Zhou

et al. 2020

npj Quantum Inf

Self Cite

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“…Experimentally, individual dimers were observed and analyzed earlier in diamond with NV centers (Zhao et al, 2011;Shi et al, 2014;Ma and Liu, 2016). Recently, they have been studied in detail in (Abobeih et al, 2018;Bradley et l., 2019;Yang et al, 2020;Bartling et al, 2022;van de Stolpe et al, 2023) where a single NV center was used not only to detect and to characterize multiple 13 C nuclear spins and few individual 13 C-13 C dimers in the specific spin environment of the studied NV center with sub-Hertz spectral resolution but also to initialize, control and readout the states of these dimers (Abobeih et al, 2018;Yang et al, 2020). In particular, in (Bradley et al, 2019), it was shown that the inhomogeneous dephasing time for the studied 13 C-13 C dimers was about 1 min at room temperature, the longest reported for individually controlled qubits.…”

Section: Introductionmentioning

confidence: 99%

Indirect interaction of 13C nuclear spins in diamond with NV centers: simulation of the full J-coupling tensors

Nizovtsev,

Pushkarchuk,

Kuten

et al. 2024

Front. Quantum Sci. Technol.

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Recent experiments on the detection, imaging, characterization and control of multiple 13C nuclear spins, as well as of individual 13C–13C dimers in diamond using a single nitrogen-vacancy (NV) center as a sensor, along with the impressive progress in increasing the spectral resolution of such sensor (up to sub-Hertz), have created a request for detailed knowledge of all possible spin interactions in the studied systems. Here, we focus on the indirect interaction (J-coupling) of 13C nuclear spins in diamond, which was not previously taken into account in studies of NV centers. Using two different levels of the density functional theory (DFT), we simulated the full tensors nJKL (K, L = X, Y,Z), describing n-bond J-coupling of nuclear spins 13C in H-terminated diamond-like clusters C10H16 (adamantane) and C35H36, as well as in the cluster C33[NV−]H36 hosting the negatively charged NV− center. We found that, in addition to the usually considered isotropic scalar nJ-coupling constant, the anisotropic contributions to the nJ-coupling tensor are essential. We also showed that the presence of the NV center affects the J-coupling characteristics, especially in the case of 13C–13C pairs located near the vacancy of the NV center.

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“…These results enable efficient imaging of complex spin samples and automatic characterization of large spin-qubit registers.Recent advances in the control of single electron spins associated to defects in solids have enabled the sensing, imaging and control of individual nuclear spins [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16] . From a quantum sensing perspective, this has enabled the detection and imaging of nuclear spins with atomic-scale resolution and single spin sensitivity, in systems of up to 27 spins 5,10,[17][18][19][20] . From a quantum information perspective, controlling individual nuclear spins provides quantum registers for quantum computation and opticallyconnected quantum networks 15,[21][22][23][24] .…”

mentioning

confidence: 99%

Deep learning enhanced individual nuclear-spin detection

et al. 2021

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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.

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Structural Analysis of Nuclear Spin Clusters via 2D Nanoscale Nuclear Magnetic Resonance Spectroscopy

Cited by 9 publications

References 37 publications

Artificial intelligence enhanced two-dimensional nanoscale nuclear magnetic resonance spectroscopy

Artificial intelligence enhanced two-dimensional nanoscale nuclear magnetic resonance spectroscopy

Indirect interaction of 13C nuclear spins in diamond with NV centers: simulation of the full J-coupling tensors

Deep learning enhanced individual nuclear-spin detection

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