As an emerging technology, quantum networks have the potential to revolutionize secure communication and data transmission technologies. In the Noisy Intermediate-Scale Quantum (NISQ) era, quantum noise causing low fidelity remains challenging in quantum networks. In this paper, we propose QLSel, an efficient selection algorithm for the high-fidelity link in the wild without assumptions about the fidelity distribution. We design the tailored link exploration strategy and link selection probability based on the coefficient of variation and Thompson sampling, to cope with the exploration-exploitation trade-off dilemma in the Multi-Armed Bandit (MAB) problem (for problem modeling). Extensive experiments demonstrate that QLSel significantly outperforms existing representative methods. Our codes and video are available at https://github.com/Secbrain/QLSel.

QLSel: Demonstrating Efficient High-Fidelity Link Selection for Quantum Networks in the Wild