Nonlinear all-optical machine learning systems and methods using nonlinear optical resonator-based neurons

Abstract

A nonlinear all optical machine learning system (NAOMLS) with nonlinear optical resonator-based continuous wave and / or spiking neurons (NORs) and linear optical components or layers (LOL) learns to implement target tasks after machine learning based direct and / or indirect inverse design and / or optimization of the NORs and / or optimization of the LOL. The inversely designed and / or optimized NORs and the optimized LOL collectively define learnable mapping functions between input lights and output lights of the NAOMLS to meet target objectives for target tasks. In some embodiments, the NORs are indirectly and inversely designed to meet inversely designed objectives in order to be integrated with the NAOMLS so that the NAOMLS can function properly to meet target objectives for target tasks. In some embodiments, the NORs are integrated directly with the NAOMLS and inversely designed and / or optimized with the LOL to directly meet target objectives for target tasks.