Polariton lattices as binarized neuromorphic networks  

作　　者：Evgeny Sedovo Alexey Kavokin 

机构地区：[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,Hangzhou310030 Zhejiang Province,China [4]Abrikosov Center for Theoretical Physics,Moscow Institute of Physics and Technology,Dolgoprudny 141701 Moscow Region,Russia

出　　处：《Light(Science & Applications)》2025年第2期384-400,共17页光(科学与应用)(英文版)

基　　金：support of Saint-Petersburg State University(research grant No.1024022800259-7);the state assignment in the field of scientific activity of the Ministry of Science and Higher Education of the Russian Federation(theme FZUN-2024-0019,state assignment of VISU);the Innovation Program for Quantum Science and Technology 2023ZD0300300 are acknowledged。

摘　　要：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：spatial coherence nonlinear r binary frameworkwhere binary neuron switching mechanismdriven neuromorphic network architecture polariton lattices nonresonant optical pumpingthe neuromorphic networks 

分 类 号：O469[理学—凝聚态物理]