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.
Abstract Satellite-based quantum key distribution (QKD) faces the challenge of wavefront aberration in free-space links. The aberration causes a mismatch between the mode field of the received beam and the single mode fiber (SMF), thereby reducing the coupling efficiency of the SMF and affecting the system’s performance. Here, adaptive optics (AO) based on diffractive deep neural network (D2NN) is applied to satellite-based QKD system to achieve rapid and effective wavefront correction. Simulation results show that the coupling efficiency has been improved under different turbulence intensities, demonstrating and large dynamic range of D2NN. Especially under strong turbulence, coupling efficiency has led to an order-of-magnitude increase in the average key rate of satellite-based QKD. Compared to AO based on adaptive stochastic parallel gradient descent (ASPGD) algorithm, D2NN AO has the advantages of light-speed real-time correction and stability. Furthermore, using reflective D2NN compensates for low transmittance of the network layers. D2NN AO provids a highly promising solution for the future development of satellite quantum communication.
Quantum key distribution (QKD) allows secure key exchange based on the principles of quantum mechanics, with higher-dimensional photonic states offering enhanced channel capacity and resilience to noise. Free-space QKD is crucial for global networks where fibres are impractical, but atmospheric turbulence introduces severe states distortions, particularly for spatial modes. Adaptive optics (AO) provides a pathway to correct these errors, though its effectiveness depends on the encoding basis. Here, we experimentally evaluate a high-speed AO system for orbital angular momentum (OAM) modes, mutually unbiased bases (MUB), and symmetric, informationally complete, positive operator-valued measures (SIC-POVM) up to dimension
d=8 in a turbulent free-space channel. While OAM states are strongly distorted, their cylindrical symmetry makes them optimally corrected by AO, yielding error rates below QKD security thresholds. MUB and SIC-POVM exhibit greater intrinsic robustness to turbulence but are less precisely corrected, though their performance remains within protocol tolerances. These results establish AO as a key enabler of secure, high-dimensional QKD and highlight the role of basis choice in optimizing resilience and correction.