Orbital angular momentum-mediated machine learning for high-accuracy mode-feature encoding

Xinyuan Fang; Xiaonan Hu; Baoli Li; Hang Su; Ke Cheng; Haitao Luan; Min Gu

doi:10.1038/s41377-024-01386-5

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Orbital angular momentum-mediated machine learning for high-accuracy mode-feature encoding

Original Articles

- Orbital angular momentum-mediated machine learning for high-accuracy mode-feature encoding
- Light: Science & Applications Vol. 13, Issue 3, Pages: 466-477(2024)
- Affiliations：
  
  1.Institute of Photonic Chips, University of Shanghai for Science and Technology, Shanghai 200093, China
  2.Centre for Artificial-Intelligence Nanophotonics, School of Optical-Electrical and Computer Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China
- Author bio：
  
  Xinyuan Fang (xinyuan.fang@usst.edu.cn)
  Min Gu (gumin@usst.edu.cn)
- Funds：
- DOI：10.1038/s41377-024-01386-5
  CLC：
- Received：28 August 2023，
  
  Revised：06 January 2024，
  
  Accepted：2024-01-16，
  
  Published Online：14 February 2024，
  
  Published：31 March 2024
- Accepted：
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Fang, X. Y. et al. Orbital angular momentum-mediated machine learning for high-accuracy mode-feature encoding.,Light: Science & Applications, 13, 466-477 (2024).
DOI：

Fang, X. Y. et al. Orbital angular momentum-mediated machine learning for high-accuracy mode-feature encoding.,Light: Science & Applications, 13, 466-477 (2024). DOI： 10.1038/s41377-024-01386-5.

摘要

Abstract

Machine learning with optical neural networks has featured unique advantages of the information processing including high speed

ultrawide bandwidths and low energy consumption because the optical dimensions (time

space

wavelength

and polarization) could be utilized to increase the degree of freedom. However

due to the lack of the capability to extract the information features in the orbital angular momentum (OAM) domain

the theoretically unlimited OAM states have never been exploited to represent the signal of the input/output nodes in the neural network model. Here

we demonstrate OAM-mediated machine learning with an all-optical convolutional neural network (CNN) based on Laguerre-Gaussian (LG) beam modes with diverse diffraction losses. The proposed CNN architecture is composed of a trainable OAM mode-dispersion impulse as a convolutional kernel for feature extraction

and deep-learning diffractive layers as a classifier. The resultant OAM mode-dispersion selectivity can be applied in information mode-feature encoding

leading to an accuracy as high as 97.2% for MNIST database through detecting the energy weighting coefficients of the encoded OAM modes

as well as a resistance to eavesdropping in point-to-point free-space transmission. Moreover

through extending the target encoded modes into multiplexed OAM states

we realize all-optical dimension reduction for anomaly detection with an accuracy of 85%. Our work provides a deep insight to the mechanism of machine learning with spatial modes basis

which can be further utilized to improve the performances of various machine-vision tasks by constructing the unsupervised learning-based auto-encoder.

关键词

Keywords

references

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