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arXiv · 2108.10067

Deep-learning-based recognition of multi-singularity structured light

Hao Wang·Xilin Yang·Zeqi Liu·Yijie Shen·Jing Pan·Yuan Meng·Zijian Shi·Zhensong Wan·Hengkang Zhang·Xing Fu·Qiang Liu
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Abstract

Structured light with customized complex topological pattern inspires diverse classical and quantum investigations underpinned by accurate detection techniques. However, the current detection schemes are limited to vortex beam with simple phase singularity. The precise recognition of general structured light with multiple singularities remains elusive. Here, we report a deep learning (DL) framework that can unveil multi-singularity phase structures in an end-to-end manner after feeding only two intensity patterns upon beam propagation captured via a camera, thus unleashing intuitive information of twisted photons. The DL toolbox can also acquire phases of Laguerre-Gaussian (LG) modes with single singularity and other general phase objects likewise. Leveraging this DL platform, a phase-based optical secret sharing (OSS) protocol is proposed, which is based on a more general class of multi-singularity modes than conventional LG beams. The OSS protocol features strong security, wealthy state space and convenient intensity-based measurements. This study opens new avenues for vortex beam communications, laser mode analysis, microscopy, Bose-Einstein condensates characterization, etc.

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Hao Wang, Xilin Yang, Zeqi Liu, Yijie Shen, Jing Pan, Yuan Meng, Zijian Shi, Zhensong Wan, Hengkang Zhang, Xing Fu, Qiang Liu. 2021-08-23. Deep-learning-based recognition of multi-singularity structured light. https://arxiv.org/abs/2108.10067

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