Statistical verifications and deep-learning predictions for satellite-to-ground quantum atmospheric channels

Abstract

This study confirms that a classical channel model can be used for describing random fluctuations in LEO-to-ground quantum atmospheric channels. It shows that practical engineering designs for future QKD missions can be conveniently conducted using the verified channel model, and that deep learning can predict channel fluctuations.