摘要
Modern machine-learning applications require huge artificial networks demanding computational power and memory.Light-based platforms promise ultrafast and energy-efficient hardware,which may help realize nextgeneration data processing devices.However,current photonic networks are limited by the number of inputoutput nodes that can be processed in a single shot.This restricted network capacity prevents their application to relevant large-scale problems such as natural language processing.Here,we realize a photonic processor for supervised learning with a capacity exceeding 1.5×10^(10)optical nodes,more than one order of magnitude larger than any previous implementation,which enables photonic large-scale text encoding and classification.By exploiting the full three-dimensional structure of the optical field propagating in free space,we overcome the interpolation threshold and reach the over-parameterized region of machine learning,a condition that allows high-performance sentiment analysis with a minimal fraction of training points.Our results provide a novel solution to scale up light-driven computing and open the route to photonic natural language processing.
基金
Museo Storico della Fisica e Centro Studi e Ricerche Enrico Fermi
Ministero dell’Universitàe della Ricerca(PRIN No.20177PSCKT)。
