基于光学衍射神经网络的拉盖尔–高斯光束识别

拉盖尔–高斯（Laguerre-Gaussian，LG）光束除了轨道角动量（orbital angular momentum，OAM）维度外，还拥有径向量子数$ p $，因此LG光束可以为光通信和光计算等应用提供更多的物理自由度。但目前常见的干涉、衍射机制的LG光束模式探测方法在受到大气湍流的干扰时，识别准确率会明显下降，从而限制了其实际应用。提出了一种基于衍射神经网络（diffractive neural network，DNN）的LG光束识别方式，实现了$ p $在$ 1～3 $范围内的识别。即使在强湍流强度，衍射距离为5 m的情况下，该识别方式的识别准确率依然能达到95%以上。该DNN方法能够为准确识别LG光束模式提供有效途径，在大容量OAM通信、高维量子信息处理等方面均具有潜在应用价值。

Laguerre-Gaussian beam recognition based on optical diffractive neural network

Laguerre-Gaussian (LG) beams possess radial quantum number p in addition to orbital angular momentum (OAM) dimension, and thus LG beams can provide more physical degrees of freedom for applications such as optical communication and optical computing. However, the recognition accuracy of the LG beam pattern detection method, which is commonly used by interference and diffraction mechanisms, is significantly reduced when it is disturbed by atmospheric turbulence (AT), which limits its practical application. We propose a diffraction neural network (DNN)-based LG beam recognition method that achieves p in the range of 1-3. Even in the case of strong turbulence intensity and diffraction distance of 5 m, the recognition accuracy still reaches more than 95%. This DNN method can provide an effective way to accurately identify LG beam patterns, and has potential applications in high-capacity OAM communication and high-dimensional quantum information processing.