Polariton lattices as binarized neuromorphic networks
Evgeny Sedov Alexey Kavokin
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%.