ML-Optimized QKD Frequency Assignment for Efficient Quantum-Classical Coexistence in Multi-Band EONs

Abstract

Quantum key distribution (QKD) is a cutting-edge technology that guarantees unbreakable security. Multi-band transmission across the O+E+S+C+L bands offers a viable solution for the coexistence of quantum and classical signals over existing fiber infrastructure. However, the secure key rate (SKR) achievable in quantum channels (QChs) is influenced by variations in classical traffic load and its spectrum usage patterns. To support dynamic and time-varying classical traffic, it is essential to estimate the achievable SKR for each QCh in real-time, enabling the selection of the optimal frequency that maximizes SKR. Conventional methods rely on solving complex integral noise equations to estimate SKR, but their computational complexity makes them unsuitable for real-time operations. In this letter, we propose a machine learning (ML) algorithm to evaluate the SKR of QChs, taking into account the time-varying behavior of classical traffic, and to select the optimal frequency for QChs. We implement three ML algorithms across various fiber intervals, all of which estimate the optimal frequency for QChs with 99% accuracy and perform computations in an average of 0.09 seconds— significantly faster than the conventional method, which has a mean computation time of 637 seconds.