Adaptive optics based on all-optical machine learning enhances the performance of satellite-based quantum key distribution

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 (D 2 NN) 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 D 2 NN. 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, D 2 NN AO has the advantages of light-speed real-time correction and stability. Furthermore, using reflective D 2 NN compensates for low transmittance of the network layers. D 2 NN AO provids a highly promising solution for the future development of satellite quantum communication.