基于机器学习的星地量子通信成码率预测及实验验证

Prediction and experimental verification for satellite-to-ground quantum communication key rate based on machine learning

星地量子通信已经验证了广域量子通信网络的可行性,面对未来量子通信网络多用户的特点,能够准确、快速预测成码率是高效利用星地量子网络资源的核心问题。提出了一种基于机器学习及恒星星像图像识别的信道预测新方法,并将此方法应用于北京地面站的观测中。实验结果表明,恒星星像的图像识别正确率可达88%,并给出是否开展星地实验的建议。在建议开展星地对接的信道情况下,预估该时段量子卫星北京地面站在仰角39.5°的筛选成码率约为8~9 kbit/s,实际星地量子通信实验的筛选成码率为8.8 kbit/s。实验结果可用于合理安排多颗卫星、多个地面站的星地对接任务,提高星地量子通信的成功率,避免浪费卫星和地面站资源,推动量子通信卫星组网的实用化研究。

Satellite-to-ground quantum key distribution(QKD)has verified the feasibility of wide-area quantum communication networks.Towards to the future multi-users of quantum communication networks,being able to accurately and quickly predict the key rate is the core issue for quantum network.This paper proposes a new channel prediction method based on machine learning and stellar image recognition,and applies this method to the observation of the Beijing ground station.The experimental results show that the stellar image recognition accuracy rate can reach 88%,and provide the suggestion on whether to carry out a QKD experiment.In the case of the recommended channel for satellite-toground QKD,it is estimated that the average rate of sifted key at elevation angle of 39.5°is about 8~9 kbit/s,and the measured sifted key rate is 8.8 kbit/s.The experimental results can be used to reasonably arrange satellite-to-ground QKD tasks of multiple satellites and multiple ground stations.Moreover,this work can improve the success rate of satellite-to-ground quantum communication,avoid wasting satellite and ground station resources,and promote the practical research of satellite-based quantum communication networking.