Orbital-angular-momentum-based optical clustering via nonlinear optics

Abstract

Machine learning offers a convenient and intelligent tool for a variety of applications in the fields ranging from fundamental research to financial analysis. With the explosive growth of data streams, i.e., “big data,” optical machine learning with the inherent capacity for massive parallel processing is gradually attracting attention. Despite significant experimental and theoretical progress in this area, limited by the coherent manipulation of multibeams, high dimensional optical vector or matrix operation is still challenging. Here, by using the second harmonic generation of high dimensional orbital angular momentum superposition states, we present a compact and robust optical clustering machine, which is the crucial component in machine learning. In experiment, we conduct supervised clustering for classification of three- and eight-dimensional vectors and unsupervised clustering for text mining of 14-dimensional texts both with high accuracies. The presented optical clustering scheme could offer a pathway for constructing high speed and low energy consumption machine learning architectures.