Relative Wavefront Error Correction Over a 2.4 km Free-Space Optical Link via Machine Learning

TL;DR

This paper demonstrates machine learning-based wavefront correction over a 2.4 km atmospheric link, reducing phase errors and potentially enhancing quantum key distribution secure rates.

Contribution

It presents experimental evidence of relative wavefront errors and introduces ML algorithms for phase correction in free-space optical links.

Findings

Up to 2/3 reduction in relative phase error variance.

Relative wavefront errors impact quantum key distribution performance.

ML-based correction can significantly improve secure key rates.

Abstract

In coherent optical communication across turbulent atmospheric channels, reference beacons can be multiplexed with information-encoded signals during transmission. In this case, it is commonly assumed that the wavefront distortion of the two is equivalent. In contrast to this assumption, we present experimental evidence of relative wavefront errors (WFEs) between polarization-multiplexed reference beacons and signals, after passing through a 2.4 km atmospheric link. We develop machine learning-based wavefront correction algorithms to compensate for observed WFEs, via phase retrieval, resulting in up to a 2/3 reduction in the relative phase error variance. Further, we analyze the excess noise contributions from relative WFEs in the context of continuous-variable quantum key distribution (CV-QKD), where our findings suggest that if future CV-QKD implementations employ wavefront correction algorithms similar to those reported here, an order of magnitude increase in secure key rates may be forthcoming.