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Data underlying the publication "Improved Electron-Nuclear Quantum Gates for Spin Sensing and Control"

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4TU.ResearchData2024-10-28 更新2026-04-23 收录
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https://data.4tu.nl/datasets/643ed69d-ced0-4d45-86b3-534a5b79c605/1
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资源简介:
The ability to sense and control nuclear spins near solid-state defects might enable a range of quantum technologies. Dynamically Decoupled Radio-Frequency (DDRF) control offers a high degree of design flexibility and long electron-spin coherence times. However, previous studies considered simplified models and little is known about optimal gate design and fundamental limits. Here, we develop a generalised DDRF framework that has important implications for spin sensing and control. Our analytical model, which we corroborate by experiments on a single NV center in diamond, reveals the mechanisms that govern the selectivity of gates and their effective Rabi frequencies, and enables flexible detuned gate designs. We apply these insights to numerically show a 60x sensitivity enhancement for detecting weakly coupled spins and study the optimisation of quantum gates in multi-qubit registers. These results advance the understanding for a broad class of gates and provide a toolbox for application-specific design, enabling improved quantum control and sensing.<br>This server contains the data and jupyter notebooks to reproduce the figures (see README file for instructions). Execute the notebook files (.ipynb extension) via an iPython environment. These will load the data from the .json and .npy data files to recreate the figures.<br>

对固态缺陷附近核自旋的感知与调控能力,有望为各类量子技术的实现提供支撑。动态解耦射频(Dynamically Decoupled Radio-Frequency, DDRF)调控技术具备极高的设计灵活性,且可延长电子自旋相干时长。然而此前的相关研究多采用简化模型,学界对最优量子门设计与基本极限的认知仍较为有限。本研究构建了一套通用化DDRF调控框架,其对自旋感知与调控具有重要意义。我们通过金刚石中单氮空位(NV)中心的实验验证了所提出的解析模型,该模型揭示了调控量子门选择性与有效拉比(Rabi)频率的物理机制,并支持灵活的失谐量子门设计方案。我们将这些研究结论应用于数值模拟,实现了弱耦合自旋探测灵敏度60倍的提升,并针对多量子比特寄存器中的量子门优化展开了研究。本研究成果深化了学界对广泛量子门品类的认知,同时提供了面向特定应用场景的设计工具箱,助力实现更优异的量子调控与感知性能。<br>本服务器包含用于复现实验图表的数据集与Jupyter Notebook文件(操作说明详见README文件)。请在iPython环境中运行扩展名为.ipynb的Notebook文件,此类文件将从.json与.npy格式的数据文件中加载数据,进而复现全部实验图表。
提供机构:
Demetriou, Nicolas; Rodríguez-Pardo Montblanch, Alejandro; Yun, Jiwon; Fortuin, Thomas; Taminiau, Tim
创建时间:
2024-10-28
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