  Integrating 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 one hand, the
conventional static channel allocation schemes cannot guarantee the quality of
quantum channels in the presence of the timevarying noises. 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. To improve the accuracy and scalability of the ML framework, we also
optimize the method of feature extraction during the training process. The
performance evaluation results indicate that the proposed scheme can
effectively resist the dynamic noise impacts in the realistic optical networks
and obtain higher secure key rate with less operation complexity than the
previous schemes.

Quantum Physics