Robust Belief-State Policy Learning for Quantum Network Routing Under Decoherence and Time-Varying Conditions

2025-09-10

AI Summary

To address insufficient routing robustness in dynamic quantum networks—caused by strong decoherence, time-varying noise, and partial observability of quantum states—this paper proposes a unified learning framework integrating Graph Neural Networks (GNNs) with Partially Observable Markov Decision Processes (POMDPs). Our approach innovatively employs GNNs to jointly encode network topology and quantum state evolution dynamics, while incorporating belief-state modeling and noise-adaptive policy updates to ensure policy convergence and decision robustness under non-stationary conditions. Experiments on simulated quantum networks with up to 100 nodes demonstrate that the proposed method significantly improves routing fidelity and entanglement distribution rate, outperforming state-of-the-art baseline approaches. Key contributions include: (i) a GNN-based representation learning scheme capturing both structural and dynamical quantum features; (ii) a POMDP formulation enhanced with adaptive belief updating under time-varying noise; and (iii) empirical validation of superior scalability and robustness in large-scale, realistic quantum networking scenarios.

Technology Category

• Planning, Routing, and Scheduling: Planning with Markov Models (MDPs, POMDPs)
• Machine Learning: Quantum Machine Learning
• Search and Optimization: Learning to Search

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

This paper presents a feature-based Partially Observable Markov Decision Process (POMDP) framework for quantum network routing, combining belief-state planning with Graph Neural Networks (GNNs) to address partial observability, decoherence, and scalability challenges in dynamic quantum systems. Our approach encodes complex quantum network dynamics, including entanglement degradation and time-varying channel noise, into a low-dimensional feature space, enabling efficient belief updates and scalable policy learning. The core of our framework is a hybrid GNN-POMDP architecture that processes graph-structured representations of entangled links to learn routing policies, coupled with a noise-adaptive mechanism that fuses POMDP belief updates with GNN outputs for robust decision making. We provide a theoretical analysis establishing guarantees for belief convergence, policy improvement, and robustness to noise. Experiments on simulated quantum networks with up to 100 nodes demonstrate significant improvements in routing fidelity and entanglement delivery rates compared to state-of-the-art baselines, particularly under high decoherence and nonstationary conditions.

Problem

• Addressing quantum network routing under decoherence and time-varying conditions
• Solving partial observability and scalability challenges in dynamic quantum systems
• Improving routing fidelity and entanglement delivery rates under noise

Innovation

• Feature-based POMDP framework for quantum routing
• Hybrid GNN-POMDP architecture with graph processing
• Noise-adaptive mechanism combining belief updates with GNN