We propose an adaptive quantum routing protocol designed for dynamic network topologies in which link fidelity and availability vary stochastically over time. By introducing a novel Link Stability Metric that integrates predictive fidelity estimation with historical reliability analysis, the protocol can make real-time routing decisions that balance communication quality, latency, and quantum resource consumption. The routing problem is formulated as a Markov Decision Process and solved using a deep reinforcement learning (RL) framework, enabling intelligent entanglement path selection and adaptive switching in response to channel degradation. We derive a fundamental bound on achievable end-to-end fidelity in dynamic quantum networks and provide convergence guarantees for the learning-based policy. Extensive simulations on scale-free and random geometric networks demonstrate significant improvements in success probability, fidelity, and latency compared with state-of-the-art benchmarks, validating the theoretical analysis and showing the potential of adaptive learning approaches for robust, scalable quantum internetworking.
Huang et al. (Wed,) studied this question.