利用深度学习识别不同传输距离的衍射涡旋光束
Identification of diffracted vortex beams at different propagation distances using deep learning
摘要：
光的轨道角动量在量子技术中，特别是在量子通信和量子传感与测距中，被认为是一种宝贵的资源。然而，光的OAM状态容易受到不期望的实验条件（诸如传播距离和相位失真）的影响，这阻碍了相关技术的现实实现的潜力。在本文中，我们利用增强的深度学习神经网络来识别具有相位失真的多个传播距离处的不同OAM模式。具体来说，我们训练的深度学习神经网络可以有效地识别涡旋光束的拓扑电荷和传播距离，准确率为97%。我们的技术具有重要意义的OAM为基础的通信和传感协议。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
