Two-dimensional nuclear magnetic resonance (NMR) is indispensable to molecule structure determination. Nitrogen-vacancy center in diamond has been proposed and developed as an outstanding quantum sensor to realize NMR in nanoscale or even single molecule. However, like conventional multi-dimensional NMR, a more efficient data accumulation and processing method is necessary to realize applicable two-dimensional (2D) nanoscale NMR with a high spatial resolution nitrogen-vacancy sensor. Deep learning is an artificial algorithm, which mimics the network of neurons of human brain, has been demonstrated superb capability in pattern identifying and noise canceling. Here we report a method, combining deep learning and sparse matrix completion, to speed up 2D nanoscale NMR spectroscopy. The signal-to-noise ratio is enhanced by 5.7 ± 1.3 dB in 10% sampling coverage by an artificial intelligence protocol on 2D nanoscale NMR of a single nuclear spin cluster. The artificial intelligence algorithm enhanced 2D nanoscale NMR protocol intrinsically suppresses the observation noise and thus improves sensitivity.

二维核磁共振（NMR）对于分子结构的确定是必不可少的。已经提出并开发了金刚石中的氮空位中心，作为一种出色的量子传感器，可以实现纳米级甚至单个分子的NMR。但是，像常规的多维NMR一样，必须使用更有效的数据累积和处理方法，才能以高空间分辨率的氮空位传感器实现适用的二维（2D）纳米NMR。深度学习是一种模拟人脑神经元网络的人工算法，已被证明具有出色的模式识别和噪声消除能力。在这里，我们报告了一种结合深度学习和稀疏矩阵完成的方法来加快2D纳米级NMR光谱分析的速度。信噪比提高5.7±1。通过人工智能协议在单个核自旋簇的二维纳米NMR上以10％的采样率覆盖3 dB。人工智能算法增强的2D纳米NMR协议从本质上抑制了观察噪声，从而提高了灵敏度。