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.

翻译：量子系统与其环境的非期望相互作用通常会导致叠加态随时间的相干性衰减。精确了解环境诱导噪声的频谱成分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，利用神经网络可以显著提高噪声谱学的精度，通过重构表征金刚石中氮-空位（NV）中心周围碳杂质集合的功率谱密度。神经网络基于NV中心在不同Carr-Purcell序列（通常用于动态去耦）下的自旋相干函数进行训练。结果表明，深度学习模型比标准动态去耦噪声谱学技术具有更高的准确性，同时所需的动态去耦序列数量大幅减少。