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

arXiv: https://arxiv.org/abs/2012.05647 · HTML: https://mlsanity.com/paper/2012.05647/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Dec 2020.

## Built on by (most cited first, 1 of 1)

- [Model-free reinforcement learning with noisy actions for automated experimental control in optics](https://mlsanity.com/paper/2405.15421/) (2024)

## Abstract (arXiv)

An innovative scheme is proposed for the dynamic control of phase in two-dimensional laser beam array. It is based on a simple neural network that predicts the complex field array from the intensity of the induced scattered pattern through a phase intensity transformer made of a diffuser. Iterated phase corrections are applied on the laser field array by phase modulators via a feedback loop to set the array to prescribed phase values. 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 1/30 of the wavelength.