Fidelity-Aware Channel Allocation in Quantum Repeater Networks using Recursive and Clustered QAOA

Quantum repeater networks(QRN) require efficient resource allocation to maintain highentanglement fidelity under noise and interference. We formulate this problem as a physicsinformedquadratic unconstrained binary optimization (QUBO) and solve it using the QuantumApproximate Optimization Algorithm (QAOA). To enable scalability on Noisy Intermediate-Scale Quantum (NISQ) devices, we introduce a cluster-based variant (CLQAOA) that decomposesthe global problem into smaller subproblems. We compare CLQAOA with classicalmethods, including Greedy and Simulated Annealing, and observe that it achieves nearoptimalsolution quality with controlled runtime scaling. We further incorporate RecursiveQAOA (RQAOA), which improves solution quality through iterative variable reduction butincurs higher computational cost. These results demonstrate that hybrid, cluster-basedquantum optimization provides a practical pathway for scalable quantum repeater networkcontrol, balancing solution quality and computational efficiency.