Adaptive Quantum Routing under Dynamic Topological Constraints

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.