Machine Learning-Based Classification of Vector Vortex Beams
Machine Learning-Based Classification of Vector Vortex Beams
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DOI：
10.1103/physrevlett.124.160401
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发表时间：
2020-04-20
影响因子：
8.6
通讯作者：
Sciarrino, Fabio
中科院分区：
文献类型：
--
作者：
Giordani, Taira;Suprano, Alessia;Sciarrino, Fabio
结构光在经典光学和量子光学中有着广泛的应用。由于光学偏振和轨道角动量之间的非平凡相关性，所谓的矢量涡旋光束在这两种情况下都显示出特殊的性质。在这里，我们展示了一个新的，灵活的实验方法的分类涡旋矢量光束。我们首先描述了一个平台，用于产生受光子量子行走启发的任意复矢量涡旋光束。然后，我们利用最近的机器学习方法，即卷积神经网络和主成分分析，识别和分类特定的偏振模式。我们的研究证明了使用基于机器学习的协议来构建和表征量子协议的高维资源所带来的显著优势。
Structured light is attracting significant attention for its diverse applications in both classical and quantum optics. The so-called vector vortex beams display peculiar properties in both contexts due to the nontrivial correlations between optical polarization and orbital angular momentum. Here we demonstrate a new, flexible experimental approach to the classification of vortex vector beams. We first describe a platform for generating arbitrary complex vector vortex beams inspired to photonic quantum walks. We then exploit recent machine learning methods-namely, convolutional neural networks and principal component analysis-to recognize and classify specific polarization patterns. Our study demonstrates the significant advantages resulting from the use of machine learning-based protocols for the construction and characterization of high-dimensional resources for quantum protocols.