Large-scale photonic computing with nonlinear disordered media

Abstract

We propose and experimentally demonstrate a large-scale, high-performance photonic computing platform that simultaneously combines light scattering and optical nonlinearity. The core processing unit consists in a disordered polycrystalline lithium niobate slab bottom-up assembled from nanocrystals. Assisted by random quasi-phase-matching, nonlinear speckles are generated as the complex interplay between the simultaneous linear random scattering and the second-harmonic generation based on the quadratic optical nonlinearity of the material. Compared to linear random projection, such nonlinear feature extraction demonstrates universal performance improvement across various machine learning tasks in image classification, univariate and multivariate regression, and graph classification.