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
- Orbital angular momentum-mediated machine learning for high-accuracy mode-feature encoding
- LSA 2024年13卷第3期页码：466-477
- 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
  中图分类号：
- 收稿日期：2023-08-28，
  
  修回日期：2024-01-06，
  
  录用日期：2024-01-16，
  
  网络出版日期：2024-02-14，
  
  纸质出版日期：2024-03-31
- Accepted：
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Orbital angular momentum-mediated machine learning for high-accuracy mode-feature encoding[J]. LSA, 2024,13(3):466-477.

Fang, X. Y. et al. Orbital angular momentum-mediated machine learning for high-accuracy mode-feature encoding.,Light: Science & Applications, 13, 466-477 (2024).
Orbital angular momentum-mediated machine learning for high-accuracy mode-feature encoding[J]. LSA, 2024,13(3):466-477. DOI： 10.1038/s41377-024-01386-5.

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

LeCun, Y., Bengio, Y. & Hinton, G. Deep learning. Nature 521 , 436–444 (2015)..

Wright, L. G. et al. Deep physical neural networks trained with backpropagation. Nature 601 , 549–555 (2022)..

Bogaerts, W. et al. Programmable photonic circuits. Nature 586 , 207–216 (2020)..

Hamerly, R. et al. Large-scale optical neural networks based on photoelectric multiplication. Phys. Rev. X 9 , 021032 (2019)..

Shen, Y. C. et al. Deep learning with coherent nanophotonic circuits. Nat. Photonics 11 , 441–446 (2017)..

Feldmann, J. et al. All-optical spiking neurosynaptic networks with self-learning capabilities. Nature 569 , 208–214 (2019)..

Li, J. X. et al. Polarization multiplexed diffractive computing: all-optical implementation of a group of linear transformations through a polarization-encoded diffractive network. Light Sci. Appl. 11 , 153 (2022)..

Ni, J. C. et al. Multidimensional phase singularities in nanophotonics. Science 374 , eabj0039 (2021)..

Forbes, A., de Oliveira, M. & Dennis, M. R. Structured light. Nat. Photonics 15 , 253–262 (2021)..

Fang, X. Y. et al. Nanophotonic manipulation of optical angular momentum for high-dimensional information optics. Adv. Opt. Photonics 13 , 772–833 (2021)..

Torner, L., Torres, J. P. & Carrasco, S. Digital spiral imaging. Opt. Express 13 , 873–881 (2005)..

Bozinovic, N.et al. Terabit-scale orbital angular momentum mode division multiplexing in fibers. Science 340 , 1545–1548 (2013)..

Ren, H. R. et al. Complex-amplitude metasurface-based orbital angular momentum holography in moment um space. Nat. Nanotechnol. 15 , 948–955 (2020)..

Sun, K. et al. Three-dimensional direct lithography of stable perovskite nanocrystals in glass. Science 375 , 307–310 (2022)..

Fang, X. Y., Ren, H. R. & Gu, M. Orbital angular momentum holography for high-security encryption. Nat. Photonics 14 , 102–108 (2020)..

Ren, H. R. et al. On-chip noninterference angular momentum multiplexing of broadband light. Science 352 , 805–809 (2016)..

Ouyang, X. et al. Synthetic helical dichroism for six-dimensional optical orbital angular momentum multiplexing. Nat. Photonics 15 , 901–907 (2021)..

Rego, L. et al. Generation of extreme-ultraviolet beams with time-varying orbital angular momentum. Science 364 , eaaw9486 (2019)..

Erhard, M., Krenn, M. & Zeilinger, A. Advances in high-dimensional quantum entanglement. Nature Reviews . Physics 2 , 365–381 (2020)..

Fu, S. Y. et al. Universal orbital angular momentum spectrum analyzer for beams. PhotoniX 1 , 19 (2020)..

Zhou, H. L. et al. Photonic matrix multiplication lights up photonic accelerator and beyond. Light Sci. Appl. 11 , 30 (2022)..

Kim, D. et al. Spontaneous generation and active manipulation of real-space optical vortices. Nature 611 , 48–54 (2022)..

Zhang, Z. F. et al. Tunable topological charge vortex microlaser. Science 368 , 760–763 (2020)..

Devlin, R. C. et al. Arbitrary spin-to-orbital angular momentum conversion of light. Science 358 , 896–901 (2017)..

Ji, Z. R. et al. Photocurrent detection of the orbital angular momentum of light. Science 368 , 763–767 (2020)..

Sroor, H. et al. High-purity orbital angular momentum states from a visible metasurface laser. Nat. Photonics 14 , 498–503 (2020)..

Piccardo, M. et al . Vortex laser arrays with topological charge control and self-healing of defects. Nat. Photonics 16 , 359–365 (2022)..

Zhou, N. et al. Ultra-compact broadband polarization diversity orbital angular momentum generator with 3.6 × 3.6 μm 2 footprint. Sci. Adv. 5 , eaau9593 (2019)..

Berkhout, G. C. G. et al. Efficient sorting of orbital angular momentum states of light. Phys. Rev. Lett. 105 , 153601 (2010)..

Ruffato, G., Massari, M. & Romanato, F. Multiplication and division of the orbital angular momentum of light with diffractive transformation optics. Light Sci. Appl. 8 , 113 (2019)..

Feldmann, J. et al. Parallel convolutional processing using an integrated photonic tensor core. Nature 589 , 52–58 (2021)..

Xu, X. Y. et al. 11 TOPS photonic convolutional accelerator for optical neural networks. Nature 589 , 44–51 (2021)..

Lin, X. et al. All-optical machine learning using diffractive deep neural networks. Science 361 , 1004–1008 (2018)..

Zhou, T. K. et al. Large-scale neuromorphic optoelectronic computing with a reconfigurable diffractive processing unit. Nat. Photonics 15 , 367–373 (2021)..

Liu, C. et al. A programmable diffractive deep neural network based on a digital-coding metasurface array. Nat. Electron. 5 , 113–122 (2022)..

Lecun, Y. et al. Gradient-based learning applied to document recognition. Proc. IEEE 86 , 2278–2324 (1998)..

Allen, L. et al. Orbital angular momentum of light and the transformation of Laguerre-Gaussian laser modes. Phys. Rev. A 45 , 8185–8189 (1992)..

Qiu, X. D. et al. Structured-pump-enabled quantum pattern recognition. Phys. Rev. Lett. 122 , 123901 (2019)..

Wei, D. et al. Laguerre-Gaussian transform for rotating image processing. Opt. Express 28 , 26898–26907 (2020)..

Svelto, O. Principles of Lasers , 5th edn. (New York: Springer, 2010).

Psaltis, D. et al. Holography in artificial neural networks. Nature 343 , 325–330 (1990)..

Goodman, J. W. Introduction to Fourier Optics , 3rd edn. (Englewood: Roberts and Company Publishers, 2005).

Oppenheim, A. V., Willsky, A. S. & Nawab, S. H. Signals & Systems , 2nd edn. (Upper Saddle River: Prentice-Hall, Inc., 1996).

Khalighi, M. A. & Uysal, M. Survey on free space optical communication: a communication theory perspective. IEEE Commun. Surv. Tutor. 16 , 2231–2258 (2014)..

Gibson, G. et al. Free-space information transfer using light beams carrying orbital angular momentum. Opt. Express 12 , 5448–5456 (2004)..

Wang, J. et al. Terabit free-space data transmission employing orbital angular momentum multiplexing. Nat. Photonics 6 , 488–496 (2012)..

Willner, A. E., Wang, J. & Huang, H. A different angle on light communications. Science 337 , 655–656 (2012)..

Li, J. H. et al. Investigation on orbital angular momentum mode-based beam shaping for indoor optical wireless communications. J. Lightwave Technol. 40 , 7738–7745 (2022)..

Li, B. L. et al. Orbital angular momentum holographic multicasting for switchable and secure wireless optical communication links. Opt. Express 31 , 23106–23114 (2023)..

Fu, S. Y. et al. Orbital angular momentum comb generation from azimuthal binary phases. Adv. Photonics Nexus. 1 , 016003 (2022)..

Yu, N. F. et al. Light propagation with phase discontinuities: generalized laws of reflection and refraction. Science 334 , 333–337 (2011)..

Zhao, R. Z., Huang, L. L. & Wang, Y. T. Recent advances in multi-dimensional metasurfaces holographic technologies. PhotoniX 1 , 20 (2020)..

Chen, P. et al. Simultaneous realization of dynamic and hybrid multiplexed holography via light-activated chiral superstructures. Laser Photonics Rev. 16 , 2200011 (2022)..

Liu, S. J. et al. Bi-c hiral nanostructures featuring dynamic optical rotatory dispersion for polychromatic light multiplexing. Adv. Mater. 35 , 2301714 (2023)..

Meng, Y. et al. Optical meta-waveguides for integrated photonics and beyond. Light Sci. Appl. 10 , 235 (2021)..

Ren, S. Y. et al. Photonic-chip-based dense entanglement distribution. PhotoniX 4 , 12 (2023)..

Dong, Y. B. et al. Nanoprinted diffractive layer integrated vertical-cavity surface-emitting vortex lasers with scalable topological charge. Nano Lett. 23 , 9096–9104 (2023)..

Gu, M. et al. Optically digitalized holography: a perspective for all-optical machine learning. Engineering 5 , 363–365 (2019)..

Trichili, A. et al. Optical communication beyond orbital angular momentum. Sci. Rep. 6 , 27674 (2016)..

Huang, Z. B. et al. All-optical signal processing of vortex beams with diffractive deep neural networks. Phys. Rev. Appl. 15 , 014037 (2021)..

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