Identification of Diffracted Vortex Beams at Different Propagation Distances Using Deep Learning
The Orbital angular momentum (OAM) 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 dist...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2022-03-01
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| Series: | Frontiers in Physics |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fphy.2022.843932/full |
| _version_ | 1811319031255269376 |
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| author | Heng Lv Yan Guo Zi-Xiang Yang Chunling Ding Wu-Hao Cai Chenglong You Rui-Bo Jin Rui-Bo Jin |
| author_facet | Heng Lv Yan Guo Zi-Xiang Yang Chunling Ding Wu-Hao Cai Chenglong You Rui-Bo Jin Rui-Bo Jin |
| author_sort | Heng Lv |
| collection | DOAJ |
| description | The Orbital angular momentum (OAM) 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. |
| first_indexed | 2024-04-13T12:35:17Z |
| format | Article |
| id | doaj.art-4e3c8902894448b0a88229f35f1eab05 |
| institution | Directory Open Access Journal |
| issn | 2296-424X |
| language | English |
| last_indexed | 2024-04-13T12:35:17Z |
| publishDate | 2022-03-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Physics |
| spelling | doaj.art-4e3c8902894448b0a88229f35f1eab052022-12-22T02:46:40ZengFrontiers Media S.A.Frontiers in Physics2296-424X2022-03-011010.3389/fphy.2022.843932843932Identification of Diffracted Vortex Beams at Different Propagation Distances Using Deep LearningHeng Lv0Yan Guo1Zi-Xiang Yang2Chunling Ding3Wu-Hao Cai4Chenglong You5Rui-Bo Jin6Rui-Bo Jin7Hubei Key Laboratory of Optical Information and Pattern Recognition, Wuhan Institute of Technology, Wuhan, ChinaHubei Key Laboratory of Optical Information and Pattern Recognition, Wuhan Institute of Technology, Wuhan, ChinaHubei Key Laboratory of Optical Information and Pattern Recognition, Wuhan Institute of Technology, Wuhan, ChinaHubei Key Laboratory of Optical Information and Pattern Recognition, Wuhan Institute of Technology, Wuhan, ChinaHubei Key Laboratory of Optical Information and Pattern Recognition, Wuhan Institute of Technology, Wuhan, ChinaQuantum Photonics Laboratory, Department of Physics and Astronomy, Louisiana State University, Baton Rouge, LA, United StatesHubei Key Laboratory of Optical Information and Pattern Recognition, Wuhan Institute of Technology, Wuhan, ChinaGuangdong Provincial Key Laboratory of Quantum Science and Engineering, Southern University of Science and Technology, Shenzhen, ChinaThe Orbital angular momentum (OAM) 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.https://www.frontiersin.org/articles/10.3389/fphy.2022.843932/fulldeep learningvortex beamsorbital angular momentumpropagationdiffraction |
| spellingShingle | Heng Lv Yan Guo Zi-Xiang Yang Chunling Ding Wu-Hao Cai Chenglong You Rui-Bo Jin Rui-Bo Jin Identification of Diffracted Vortex Beams at Different Propagation Distances Using Deep Learning Frontiers in Physics deep learning vortex beams orbital angular momentum propagation diffraction |
| title | Identification of Diffracted Vortex Beams at Different Propagation Distances Using Deep Learning |
| title_full | Identification of Diffracted Vortex Beams at Different Propagation Distances Using Deep Learning |
| title_fullStr | Identification of Diffracted Vortex Beams at Different Propagation Distances Using Deep Learning |
| title_full_unstemmed | Identification of Diffracted Vortex Beams at Different Propagation Distances Using Deep Learning |
| title_short | Identification of Diffracted Vortex Beams at Different Propagation Distances Using Deep Learning |
| title_sort | identification of diffracted vortex beams at different propagation distances using deep learning |
| topic | deep learning vortex beams orbital angular momentum propagation diffraction |
| url | https://www.frontiersin.org/articles/10.3389/fphy.2022.843932/full |
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