Application and performance analysis of Epsilon-Greedy optimization strategy in quantum link selection
Liang Huang, Jihao Fan · Information Sciences · 2026
We investigate the optimal selection of high-fidelity quantum links that can preserve fragile quantum states during information transmission. However, uniformly estimating the fidelities of all links becomes prohibitively costly in large-scale networks with numerous noisy connections. To overcome this limitation, we recast link selection and fidelity inference as an optimal-action discovery task within a reinforcement learning framework. Subsequently, we propose an algorithm termed Epsilon-Greedy Quantum Link Selection (EGreedyQLiS). This algorithm effectively identifies the optimal link among numerous quantum links and provides accurate fidelity estimates with a low consumption of quantum resources. EGreedyQLiS infers link fidelities using observations obtained from a standard network benchmarking procedure and greedily optimizes link selection during the fidelity estimation procedure. This optimization strategy concentrates quantum resources on estimating high-fidelity links, thereby providing accurate fidelity estimation for these links. The results of extensive simulations demonstrate that EGreedyQLiS exceeds existing approaches in optimal link identification with reduced quantum resource overhead.