The turbulence channel, as a potential medium for communications, plays a major role in establishing global quantum networks. However, the weather-tunable channel parameters have an influence on performance of quantum communication system. In this paper, we consider effects of weather conditions on attenuation of the transmission of turbulent channel and thus on performance of continuous variable quantum teleportation (CVQT). The serious weather conditions that involve snow, rain, fog and haze have been employed for security analysis of the practical system. Moreover, a linear optical amplifier is used for performance improvement in terms of the fidelity and transmission distance as well. The simulation results show that the noiseless linear optical amplifier can increase the fidelity and maximal transmission distance of the CVQT system that involves serious weather conditions. This has certain reference guides significance for information transmission in the face of various extreme weathers.

Satellite-based quantum communication is critical in establishing a global quantum network for its ability to achieve thousand-kilometer-level tasks without the requirement of quantum relay. The free-space channel, however, is affected by diffraction, regular refraction, and atmospheric turbulence, which induce random wandering and broadening of the transmitted beam, resulting in the transmittance of fluctuation. In such a scenario, the ability to predict the unknown situation has become a problem worth considering. In this paper, we suggest a machine learning (ML) assisted prediction of continuous-variable quantum teleportation (CVQT) over satellite-ground channel. We apply the prediction algorithm, which involves k-nearest neighbor (KNN) algorithm and decision tree (DT) algorithm, to the squeezing parameter and the satellite altitude. Numerical analysis shows that the predicted parameter can reflect the actual derivation and calculation under certain circumstances within the allowable range of error. Moreover, we apply the prediction algorithm to the case affected by the fluctuating parameter transmittance. The results show that the ML-assisted prediction results are consistent with the actual data when zenith angles, turbulence strengths, and wavelengths are taken into account. It demonstrates the high identifying accuracy without increasing the complexity of the system.