Experimental phase control of a 100 laser beam array with quasi-reinforcement learning of a neural network in an error reduction loop.

Abstract

An innovative scheme is proposed for the dynamic phase control of a laser beam array. It is based on a simple neural network included in a phase correction loop that predicts the complex field array from the intensity of the induced scattered pattern through a phase intensity transformer made of a diffuser. A crucial feature is the use of a kind of reinforcement learning approach for the neural network training which takes account of the iterated corrections. Experiments on a proof-of-concept system demonstrated the high performance and scalability of the scheme with an array of up to 100 laser beams and a phase setting at λ/30.