On-Demand Phase Control of a 7-Fiber Amplifiers Array with Neural Network and Quasi-Reinforcement Learning

Abstract

We report a coherent beam combining technique using a specific quasi-reinforcement learning scheme. A neural network learned by this method enables the tailoring and locking of a tiled beam array on any phase map. We present the experimental implementation of on-demand phase control by a neural network in a seven-fiber laser array. This servo loop needs only six phase corrections to converge to the desired phase set at any profile, with a bandwidth higher than 1 kHz. Moreover, we demonstrate the dynamical feature of adaptive phase control, performing sequences of controlled phase sets. It is the first time, to the best of our knowledge, that an actual array of seven-fiber amplifiers has been successfully phase-locked and controlled by machine learning.