Polariton lattices as binarized neuromorphic networks

Evgeny Sedov; Alexey Kavokin

doi:10.1038/s41377-024-01719-4

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Polariton lattices as binarized neuromorphic networks

Original Articles

- Polariton lattices as binarized neuromorphic networks
- Light: Science & Applications Vol. 14, Issue 2, Pages: 384-400(2025)
- Affiliations：
  
  1.Spin-Optics laboratory, St. Petersburg State University, St. Petersburg 198504, Russia
  2.Stoletov Vladimir State University, Vladimir 600000, Russia
  3.School of Science, Westlake University, Hangzhou3 10030 Zhejiang Province, China
  4.Abrikosov Center for Theoretical Physics, Moscow Institute of Physics and Technology, Dolgoprudny 141701 Moscow Region, Russia
- Author bio：
  
  Evgeny Sedov (evgeny_sedov@mail.ru)
- Funds：
- DOI：10.1038/s41377-024-01719-4
  CLC：
- Received：10 January 2024，
  
  Revised：08 December 2024，
  
  Accepted：2024-12-13，
  
  Published Online：16 January 2025，
  
  Published：28 February 2025
- Accepted：
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Sedov, E. & Kavokin, A. Polariton lattices as binarized neuromorphic networks. Light: Science & Applications, 14, 384-400 (2025).
DOI：

Sedov, E. & Kavokin, A. Polariton lattices as binarized neuromorphic networks. Light: Science & Applications, 14, 384-400 (2025). DOI： 10.1038/s41377-024-01719-4.

摘要

Abstract

We introduce a novel neuromorphic network architecture based on a lattice of exciton-polariton condensates

intricately interconnected and energized through nonresonant optical pumping. The network employs a binary framework

where each neuron

facilitated by the spatial coherence of pairwise coupled condensates

performs binary operations. This coherence

emerging from the ballistic propagation of polaritons

ensures efficient

network-wide communication. The binary neuron switching mechanism

driven by the nonlinear repulsion through the excitonic component of polaritons

offers computational efficiency and scalability advantages over continuous weight neural networks. Our network enables parallel processing

enhancing computational speed compared to sequential or pulse-coded binary systems. The system's performance was evaluated using diverse datasets

including the MNIST dataset for image recognition and the Speech Commands dataset for voice recognition tasks. In both scenarios

the proposed system demonstrates the potential to outperform existing polaritonic neuromorphic systems. For image recognition

this is evidenced by an impressive predicted classification accuracy of up to 97.5%. In voice recognition

the system achieved a classification accuracy of about 68% for the ten-class subset

surpassing the performance of conventional benchmark

the Hidden Markov Model with Gaussian Mixture Model.

关键词

Keywords

references

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