Hybrid Quantum-Classical Routing for QKD-ON: Leveraging QAOA and QVE for Minimizing Blocking

Abstract

Resource Allocation in quantum key distribution-enabled optical networks (QKD-ON) is an NP-hard combinatorial problem. Classical heuristics are greedy and suffer high blocking under dynamic traffic and security constraints, while deep reinforcement learning (DRL) methods can reduce blocking but introduce a processing bottleneck because their inference latency is often slower than the operational timescales of QKD-ON. We introduce hybrid quantum-classical frameworks using the quantum approximate optimization algorithm (QAOA) and variational quantum eigensolver (VQE) to perform request routing. The scheduling problem is encoded as a hamiltonian with penalty terms for wavelength and timeslot conflicts. We evaluate the approach on NSFNet and UBN topologies under dynamic traffic with time division multiplexing (TDM) and wavelength-time division multiplexing (WTDM). Our quantum-enhanced algorithms yield comparable or lower blocking with reduced inference latency, which can improve further on quantum hardware. © 2025 IEEE.