Experimental demonstration of deep-learning-enabled adaptive optics

Abstract

Satellite-based quantum communication is a promising approach for establishing global-scale quantum networks. In free-space quantum channels, single-mode-fiber coupling plays a crucial role in increasing the signal-to-noise ratio of daylight quantum key distribution (QKD) and ensuring compatibility with standard fiber-based QKD protocols. However, consistently achieving high efficiency and stable single-mode-fiber coupling under strong atmospheric turbulence remains an ongoing experimental challenge. In this study, we experimentally demonstrate an adaptive method based on convolutional neural networks capable of directly estimating phase information from a single defocused image. We developed a convolutional neural network to establish the relationship between intensity distribution and phase information of turbulent distortions. We demonstrate the real-time performance of our deep-learning adaptive method in increasing single-mode-fiber coupling efficiency across various turbulence scales and quantify turbulence frequencies. Notably, the method proved highly effective in strong-turbulence scenarios, with frequencies reaching up to 200 Hz, leading to a significant increase in single-mode-fiber coupling efficiency. We demonstrate the corrective capability of our adaptive method for strong turbulence, enabled by the generalization of the convolutional neural network. Our results offer an efficient solution for daytime free-space QKD applications.