Demand-Aware Identification of High-Fidelity Link Sets in Quantum Networks
Ensuring the reliability of quantum networks requires the accurate evaluation of fidelity-a metric representing link quality-and the selection of high-fidelity links. However, since fidelity estimation consumes a large number of measurements, an efficient method for identifying high-quality communication links with limited resources is desirable. Existing studies have primarily focused on identifying high-fidelity links between a pair of nodes, leaving adaptive resource allocation insufficiently explored in multi-destination environments where destinations differ in network importance (e.g., communication demand or the quantum memory capacity of nodes). In this study, we propose DaTopLinks (Demand-aware Top- $K$ HighFidelity Links), a method for efficiently identifying the top- $K$ destinations and their best link for each selected destination according to a utility function that combines destination importance and link fidelity. The algorithm introduces a dual-criterion link elimination mechanism that simultaneously performs intradestination link elimination and top- $K$ destination elimination. It also incorporates an early confirmation mechanism that allows early termination of measurements for destinations once their inclusion in the top- $K$ set and the identification of their best link are statistically guaranteed. In our theoretical analysis, we derive an upper bound on the sample complexity based on an effective gap that captures both inter-destination utility differences and intra-destination fidelity differences. Simulation results demonstrate that the proposed method functions effectively under depolarizing, dephasing, and bit-flip noise models.