六方氮化硼中的低对称性空位相关自旋量子比特

IF 9.4 1区 材料科学 Q1 CHEMISTRY, PHYSICAL
Rohit Babar, Gergely Barcza, Anton Pershin, Hyoju Park, Oscar Bulancea Lindvall, Gergő Thiering, Örs Legeza, Jamie H. Warner, Igor A. Abrikosov, Adam Gali, Viktor Ivády
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引用次数: 0

摘要

半导体中的点缺陷量子比特在高空间分辨率传感方面表现出了卓越的能力,引起了多学科的广泛兴趣。承载点缺陷量子比特的六方氮化硼(hBN)最近通过实施传感箔,为量子传感开辟了新天地。目前,氮化硼中的点缺陷传感器的灵敏度受到磁共振信号线宽的限制,而强超线性耦合会使线宽变宽。在这里,我们报告了一种与空位相关的自旋量子比特,它具有固有的低对称性构型,即 VB2 中心,从而在零磁场下降低了磁共振线宽。VB2 中心还配备了经典存储器,可用于存储种群信息。通过扫描透射电子显微镜成像,我们证实了独立单层 hBN 中 VB2 构型的存在。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Low-symmetry vacancy-related spin qubit in hexagonal boron nitride

Low-symmetry vacancy-related spin qubit in hexagonal boron nitride

Point defect qubits in semiconductors have demonstrated their outstanding capabilities for high spatial resolution sensing generating broad multidisciplinary interest. Hexagonal boron nitride (hBN) hosting point defect qubits have recently opened up new horizons for quantum sensing by implementing sensing foils. The sensitivity of point defect sensors in hBN is currently limited by the linewidth of the magnetic resonance signal, which is broadened due to strong hyperfine couplings. Here, we report on a vacancy-related spin qubit with an inherently low symmetry configuration, the VB2 center, giving rise to a reduced magnetic resonance linewidth at zero magnetic fields. The VB2 center is also equipped with a classical memory that can be utilized for storing population information. Using scanning transmission electron microscopy imaging, we confirm the existence of the VB2 configuration in free-standing monolayer hBN.

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来源期刊
npj Computational Materials
npj Computational Materials Mathematics-Modeling and Simulation
CiteScore
15.30
自引率
5.20%
发文量
229
审稿时长
6 weeks
期刊介绍: npj Computational Materials is a high-quality open access journal from Nature Research that publishes research papers applying computational approaches for the design of new materials and enhancing our understanding of existing ones. The journal also welcomes papers on new computational techniques and the refinement of current approaches that support these aims, as well as experimental papers that complement computational findings. Some key features of npj Computational Materials include a 2-year impact factor of 12.241 (2021), article downloads of 1,138,590 (2021), and a fast turnaround time of 11 days from submission to the first editorial decision. The journal is indexed in various databases and services, including Chemical Abstracts Service (ACS), Astrophysics Data System (ADS), Current Contents/Physical, Chemical and Earth Sciences, Journal Citation Reports/Science Edition, SCOPUS, EI Compendex, INSPEC, Google Scholar, SCImago, DOAJ, CNKI, and Science Citation Index Expanded (SCIE), among others.
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