Polariton lattices as binarized neuromorphic networks

Author:

Evgeny Sedov, Alexey Kavokin

Keyword:

Condensed Matter, Disordered Systems and Neural Networks, Disordered Systems and Neural Networks (cond-mat.dis-nn), Machine Learning (cs.LG), Neural and Evolutionary Computing (cs.NE), Applied Physics (physics.app-ph)

journal:

date:

2024-01-14 00:00:00

Abstract

We introduce a novel neuromorphic network architecture based on a lattice of exciton-polariton condensates, intricately interconnected and energized through non-resonant 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 the MNIST dataset for handwritten digit recognition, showcasing the potential to outperform existing polaritonic neuromorphic systems, as demonstrated by its impressive predicted classification accuracy of up to 97.5%.

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