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arXiv 2301.05079quant-phcs.AIcs.LGcs.NE
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深度学习增强的自旋量子比特环境的噪声光谱学

Deep learning enhanced noise spectroscopy of a spin qubit environment

European Laboratory for Non-linear Spectroscopy (LENS)(欧洲非线性光谱实验室(LENS));Massachusetts Institute of Technology(麻省理工学院)

Stefano Martina, Santiago Hernández-Gómez, Stefano Gherardini, Filippo Caruso, Nicole Fabbri

AI总结:

本文实验证明神经网络可通过重构NV中心周围碳杂质系综的功率谱密度,大幅提升噪声光谱学准确性,且比标准动力学解耦技术更准确、所需序列更少。

摘要:

量子系统与其环境之间不希望发生的相互作用通常会导致叠加态的相干性随时间衰减。精确了解环境引起的噪声的频谱内容,对于保护量子比特相干性并优化其在量子器件应用中的使用至关重要。我们通过实验证明,使用神经网络可以极大地提高噪声光谱学的准确性,方法是重构表征金刚石中氮-空位(NV)中心周围碳杂质系综的功率谱密度。神经网络在NV中心受不同Carr-Purcell序列(通常用于动力学解耦(DD))影响的自旋相干函数上进行训练。结果,我们确定深度学习模型可以比标准DD噪声光谱技术更准确,同时需要少得多的DD序列数量。

英文摘要:

The undesired interaction of a quantum system with its environment generally leads to a coherence decay of superposition states in time. A precise knowledge of the spectral content of the noise induced by the environment is crucial to protect qubit coherence and optimize its employment in quantum device applications. We experimentally show that the use of neural networks can highly increase the accuracy of noise spectroscopy, by reconstructing the power spectral density that characterizes an ensemble of carbon impurities around a nitrogen-vacancy (NV) center in diamond. Neural networks are trained over spin coherence functions of the NV center subjected to different Carr-Purcell sequences, typically used for dynamical decoupling (DD). As a result, we determine that deep learning models can be more accurate than standard DD noise-spectroscopy techniques, by requiring at the same time a much smaller number of DD sequences.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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