Multi-wavelength optical information processing with deep reinforcement learning
Abstract Multi-wavelength optical information processing systems are commonly utilized in optical neural networks and broadband signal processing. However, their effectiveness is often compromised by frequency-selective responses caused by fabrication, transmission, and environmental factors. To mit...
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| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Publishing Group
2025-04-01
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| Series: | Light: Science & Applications |
| Online Access: | https://doi.org/10.1038/s41377-025-01846-6 |
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| _version_ | 1850181229369884672 |
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| author | Qiuquan Yan Hao Ouyang Zilong Tao Meili Shen Shiyin Du Jun Zhang Hengzhu Liu Hao Hao Tian Jiang |
| author_facet | Qiuquan Yan Hao Ouyang Zilong Tao Meili Shen Shiyin Du Jun Zhang Hengzhu Liu Hao Hao Tian Jiang |
| author_sort | Qiuquan Yan |
| collection | DOAJ |
| description | Abstract Multi-wavelength optical information processing systems are commonly utilized in optical neural networks and broadband signal processing. However, their effectiveness is often compromised by frequency-selective responses caused by fabrication, transmission, and environmental factors. To mitigate these issues, this study introduces a deep reinforcement learning calibration (DRC) method inspired by the deep deterministic policy gradient training strategy. This method continuously and autonomously learns from the system, effectively accumulating experiential knowledge for calibration strategies and demonstrating superior adaptability compared to traditional methods. In systems based on dispersion compensating fiber, micro-ring resonator array, and Mach-Zehnder interferometer array that use multi-wavelength optical carriers as the light source, the DRC method enables the completion of the corresponding signal processing functions within 21 iterations. This method provides efficient and accurate control, making it suitable for applications such as optical convolution computation acceleration, microwave photonic signal processing, and optical network routing. |
| format | Article |
| id | doaj-art-6c6ee048087142db8c4c9e3dfa5a2fcc |
| institution | OA Journals |
| issn | 2047-7538 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Nature Publishing Group |
| record_format | Article |
| series | Light: Science & Applications |
| spelling | doaj-art-6c6ee048087142db8c4c9e3dfa5a2fcc2025-08-20T02:17:57ZengNature Publishing GroupLight: Science & Applications2047-75382025-04-0114111210.1038/s41377-025-01846-6Multi-wavelength optical information processing with deep reinforcement learningQiuquan Yan0Hao Ouyang1Zilong Tao2Meili Shen3Shiyin Du4Jun Zhang5Hengzhu Liu6Hao Hao7Tian Jiang8College of Computer Science and Technology, National University of Defense TechnologyInstitute for Quantum Science and Technology, College of Science, National University of Defense TechnologyCollege of Computer Science and Technology, National University of Defense TechnologyNational Innovation Institute of Defense Technology, Academy of Military Science PLACollege of Computer Science and Technology, National University of Defense TechnologyNational Innovation Institute of Defense Technology, Academy of Military Science PLACollege of Computer Science and Technology, National University of Defense TechnologyInstitute for Quantum Science and Technology, College of Science, National University of Defense TechnologyInstitute for Quantum Science and Technology, College of Science, National University of Defense TechnologyAbstract Multi-wavelength optical information processing systems are commonly utilized in optical neural networks and broadband signal processing. However, their effectiveness is often compromised by frequency-selective responses caused by fabrication, transmission, and environmental factors. To mitigate these issues, this study introduces a deep reinforcement learning calibration (DRC) method inspired by the deep deterministic policy gradient training strategy. This method continuously and autonomously learns from the system, effectively accumulating experiential knowledge for calibration strategies and demonstrating superior adaptability compared to traditional methods. In systems based on dispersion compensating fiber, micro-ring resonator array, and Mach-Zehnder interferometer array that use multi-wavelength optical carriers as the light source, the DRC method enables the completion of the corresponding signal processing functions within 21 iterations. This method provides efficient and accurate control, making it suitable for applications such as optical convolution computation acceleration, microwave photonic signal processing, and optical network routing.https://doi.org/10.1038/s41377-025-01846-6 |
| spellingShingle | Qiuquan Yan Hao Ouyang Zilong Tao Meili Shen Shiyin Du Jun Zhang Hengzhu Liu Hao Hao Tian Jiang Multi-wavelength optical information processing with deep reinforcement learning Light: Science & Applications |
| title | Multi-wavelength optical information processing with deep reinforcement learning |
| title_full | Multi-wavelength optical information processing with deep reinforcement learning |
| title_fullStr | Multi-wavelength optical information processing with deep reinforcement learning |
| title_full_unstemmed | Multi-wavelength optical information processing with deep reinforcement learning |
| title_short | Multi-wavelength optical information processing with deep reinforcement learning |
| title_sort | multi wavelength optical information processing with deep reinforcement learning |
| url | https://doi.org/10.1038/s41377-025-01846-6 |
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