Polariton Lattices as Binarized Neuromorphic Networks

Attend a research seminar exploring the innovative development of neuromorphic network architecture based on exciton-polariton condensate lattices. Discover how binary operations are performed through spatially coherent, pairwise coupled condensates, enabled by ballistic polariton propagation. Learn about the advantages of binary neuron switching mechanisms, driven by nonlinear polariton repulsion, over traditional continuous weight neural networks. Examine the system's practical applications through performance evaluations on the MNIST dataset for image recognition, achieving up to 97.5% classification accuracy, and the Speech Commands dataset for voice recognition, reaching 68% accuracy for ten-class classification. Understand how this novel approach surpasses conventional methods like the Hidden Markov Model with Gaussian Mixture Model in voice recognition tasks. The seminar, delivered in both English and Russian, provides deep insights into the intersection of quantum physics and machine learning architectures.