Applications of LightBGM part2(Machine Learning)

Abstract: This paper studies the case of big data-based intelligent product potential customer mining internal competition in China Telecom Shanghai Company. Abstract: ntegrating quantum key distribution (QKD) with existing optical networks is highly desired to reduce the deployment costs and achieve efficient resource utilization, and some pointtopoint transmitting experiments have verified its feasibility. Nevertheless, there are still many problems in the realistic scenario where QKD coexists with dynamic data traffics. On the other hand, considering the complex noise generation caused by dynamic classical data traffics with variable characters, it is challenging to achieve online high-performance quantum channel assignments. To address these problems, we propose a machine learning based noise-suppressing channel allocation (ML-NSCA) scheme. In this scheme, the LightGBM based ML framework is trained to predict the optimal channel allocations with lowest noise impacts, according to which, the quantum channels are periodically reallocated to guarantee high secure key rate.