Zu-yang Wan

Turbulence is a complex and chaotic fluid motion state. Atmospheric turbulence presents significant challenges for applications such as remote sensing,astronomical observations, and free-space quantum key distribution (QKD), due to its rapid evolution across temporal and spatial scales. Traditional methods for correcting atmospheric turbulence encounter difficulties, particularly under strong daytime turbulence conditions. In this study, we develop a deep learning-based adaptive method to correct strong atmospheric turbulence in field conditions, facilitating the turbulence correction over 1.4 km and 7 km free-space channels. Experimental results present better correction performance compared to wavefront sensor-based methods, yielding a 2–4 dB Strehl ratio improvement. Additionally, our approach directly estimates phase information from a defocused camera, significantly reducing the implementation cost of adaptive systems. Furthermore, we evaluate the performance of a daytime free-space QKD system incorporating our deep learning–based method, leading to higher key rates and longer propagation distances. Our method provides a practical and efficient solution for daytime QKD applications.