# Incoherent Light-Driven Nonlinear Optical Extreme Learner via Data Reverberation Source: [Incoherent Light-Driven Nonlinear Optical Extreme Learner via Data Reverberation]() Collection index: [llms.txt]() Data updated: 2025-10-25T08:20:02.646Z Authors: Bofeng Liu, Xu Mei, Sadman Shafi, Tunan Xia, Iam-Choon Khoo, Zhiwen Liu, Xingjie Ni Published: 2025-08-11T19:32:00.000Z ## Abstract Artificial neural networks have revolutionized fields from computer vision to natural language processing, yet their growing energy and computational demands threaten future progress. Optical neural networks promise greater speed, bandwidth, and energy efficiency, but suffer from weak optical nonlinearities. Here, we demonstrate a low-power, incoherent-light-driven optical extreme learner that leverages 'data nonlinearity' from optical pattern reverberation, eliminating reliance on intrinsic nonlinear materials. By encoding input data in the spatial polarization distribution of a tailored optical cavity and allowing light to pass through it multiple times, we achieve nonlinear transformations at extremely low optical power. Coupled with a simple trainable readout, our optical learner consistently outperforms linear digital networks in standard image classification tasks and XOR benchmarks, delivering accuracy matching fully nonlinear digital models. Our compact, energy-efficient approach significantly reduces complexity, cost, and energy consumption, paving the way for practical, scalable all-optical machine learning platforms. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()