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.

使用单个电子自旋检测核自旋已为量子感测和量子信息处理提供了各种机会。原理证明实验已经证明了核自旋样品和受控多量子位寄存器的原子级成像。但是，要对更复杂的样本成像并实现更大规模的量子处理器，就需要有效，自动地表征自旋系统的计算机化方法。在这里，我们实现了一个深度学习模型，该模型使用钻石中单个氮空位（NV）中心的电子自旋作为传感器自动识别核自旋。基于神经网络算法，我们针对高度非线性频谱开发了噪声恢复程序和训练序列。我们应用这些方法以实验证明单个NV中心周围31个核自旋的快速识别，并准确确定超精细参数。我们的方法可以扩展到更大的自旋系统，并适用于广泛的电子-核相互作用强度。这些结果为复杂自旋样本的高效成像和大型自旋量子位寄存器的自动表征铺平了道路。