Multimode fiber laser cavities as nonlinear optical processors

Abstract

Optical computing provides a promising path toward energy-efficient machine learning, yet implementing nonlinear transformations without complex electronics or high-power sources remains challenging. Here, we demonstrate that continuous-wave multimode fiber laser cavities can function as nonlinear optical processors. Input images encoded as phase patterns on a spatial light modulator undergo high-dimensional transformation through the interplay of multimode interference and gain saturation dynamics. The cavity maps input data into spatially stable, class-separable intensity distributions, enabling a simple linear classifier to achieve accuracies of 85–99% across diverse benchmarks—including medical imaging and remote sensing—with orders of magnitude fewer trainable parameters than deep neural networks. Our results establish multimode fiber lasers as compact, low-power physical processors for scalable optical machine learning. Optical computing offers an energy-efficient alternative for AI, but generating the nonlinearities for machine learning usually demands high power or digital assist. This work demonstrates that a multimode fiber laser natively performs these operations through gain saturation, enabling high accuracy medical and geospatial classification tasks.