文章基本信息

标题：Deep-learning-based recognition of multi-singularity structured light
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作者：Wang Hao ; Wang Hao ; Yang Xilin 等
期刊名称：Nanophotonics
印刷版ISSN：2192-8606
电子版ISSN：2192-8614
出版年度：2021
卷号：11
期号：4
页码：779-786
DOI：10.1515/nanoph-2021-0489
语种：English
出版社：Walter de Gruyter GmbH
摘要：Structured light with customized topological patterns inspires diverse classical and quantum investigations underpinned by accurate detection techniques. However, the current detection schemes are limited to vortex beams with a simple phase singularity. The precise recognition of general structured light with multiple singularities remains elusive. Here, we report 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. By outputting the phase directly, rich and intuitive information of twisted photons is unleashed. The DL toolbox can also acquire phases of Laguerre–Gaussian (LG) modes with a single singularity and other general phase objects likewise. Enabled by 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 large-capacity communications, laser mode analysis, microscopy, Bose–Einstein condensates characterization, etc.