Multi-wavelength optical information processing with deep reinforcement learning

Qiuquan Yan; Hao Ouyang; Zilong Tao; Meili Shen; Shiyin Du; Jun Zhang; Hengzhu Liu; Hao Hao; Tian Jiang

doi:10.1038/s41377-025-01846-6

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Multi-wavelength optical information processing with deep reinforcement learning

Original Articles

- Multi-wavelength optical information processing with deep reinforcement learning
- Light: Science & Applications Vol. 14, Issue 6, Pages: 1643-1654(2025)
- Affiliations：
  
  1.College of Computer Science and Technology, National University of Defense Technology, Changsha, China
  2.Institute for Quantum Science and Technology, College of Science, National University of Defense Technology, Changsha, China
  3.National Innovation Institute of Defense Technology, Academy of Military Science PLA, Beijing, China
  4.College of Advanced Interdisciplinary Studies, National University of Defense Technology, Changsha, China
  5.Hunan Research Center of the Basic Discipline for Physical States, National University of Defense Technology, Changsha, China
- Author bio：
  
  Jun Zhang (zhangjun12a@nudt.edu.cn)
  Hao Hao (HH65637917@163.com)
  Tian Jiang (tjiang@nudt.edu.cn)
- Funds：
- DOI：10.1038/s41377-025-01846-6
  CLC：
- Received：20 September 2024，
  
  Revised：16 March 2025，
  
  Accepted：21 March 2025，
  
  Published Online：15 April 2025，
  
  Published：30 June 2025
- Accepted：
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Yan, Q. Q. et al. Multi-wavelength optical information processing with deep reinforcement learning. Light: Science & Applications, 14, 1643-1654 (2025).
DOI：

Yan, Q. Q. et al. Multi-wavelength optical information processing with deep reinforcement learning. Light: Science & Applications, 14, 1643-1654 (2025). DOI： 10.1038/s41377-025-01846-6.

摘要

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.

关键词

Keywords

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