Fidelity-Threshold Online Path Selection and Request Scheduling in Quantum Networks

Abstract

Quantum networks are a promising technology for supporting a wide range of quantum applications with diverse service requirements, such as high fidelity, low end-to-end (E2E) latency, and high entanglement rate. However, in practice, the fidelity of entanglement is often unknown. Existing studies primarily focus on estimating fidelity and selecting the highest-fidelity path, but often overlook other application-specific requirements. To address this limitation, we propose Variance-aware Fidelity Threshold Path Selection (VaFTPS), an efficient candidate path selection algorithm that identifies paths satisfying fidelity requirements. VaFTPS exploits a key property of fidelity estimation: higher-fidelity paths yield lower-variance benchmarking measurements. It adopts a mean-variance reward formulation within a multi-armed bandit (MAB) framework. Based on benchmarking results, VaFTPS estimates entanglement fidelity of paths and eliminates paths unlikely to satisfy the fidelity threshold using a tailored confidence interval, thereby reducing resource consumption. To further support differentiated service, we design a request scheduling strategy that allocates requests to the selected candidate paths to maximize the total service rate for multiple requests while satisfying the fidelity, E2E latency, and entanglement rate requirements. Extensive experiments in NetSquid show that our approach efficiently identifies paths satisfying the fidelity requirement and provides differentiated service for various requests.