论文标题
使用深度学习在不同的传播距离处识别衍射的涡流梁
Identification of diffracted vortex beams at different propagation distances using deep learning
论文作者
论文摘要
光的光动量被认为是量子技术中的宝贵资源，尤其是在量子通信以及量子传感和范围内。但是，光OAM的光很容易受到不良的实验条件（例如传播距离和相扭曲）的影响，这阻碍了相关技术的现实实施的潜力。在本文中，我们利用了增强的深度学习神经网络，以在多个传播距离内识别不同的光线模式。具体而言，我们训练有素的深度学习神经网络可以有效地识别涡旋束的拓扑电荷和传播距离，其精度为97％。我们的技术对基于OAM的通信和传感协议具有重要意义。
Orbital angular momentum of light is regarded as a valuable resource in quantum technology, especially in quantum communication and quantum sensing and ranging. However, the OAM state of light is susceptible to undesirable experimental conditions such as propagation distance and phase distortions, which hinders the potential for the realistic implementation of relevant technologies. In this article, we exploit an enhanced deep learning neural network to identify different OAM modes of light at multiple propagation distances with phase distortions. Specifically, our trained deep learning neural network can efficiently identify the vortex beam's topological charge and propagation distance with 97% accuracy. Our technique has important implications for OAM based communication and sensing protocols.
