SatQNet: Satellite-assisted Quantum Network Entanglement Routing Using Directed Line Graph Neural Networks

2026-04-10

AI Summary

This work addresses the challenge of efficient entanglement routing in satellite-assisted quantum networks, where dynamic topologies arise from satellite motion and stochastic links. To tackle this problem, the authors propose a decentralized reinforcement learning approach that constructs local graph representations through message exchange among neighboring relay nodes. A key innovation is the introduction of an edge-oriented directed line graph neural network, which performs local message passing directly on edge embeddings to effectively capture the high connectivity and time-varying nature of quantum links. Integrating reinforcement learning, directed line graph neural networks, and a decentralized routing mechanism, the proposed method significantly outperforms existing heuristic and learning-based baselines across diverse scenarios—including a real-world European backbone network—and generalizes to unseen topologies without requiring retraining.

Technology Category

• Machine Learning: Quantum Machine Learning
• Planning, Routing, and Scheduling: Planning with Language Models
• 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

Quantum networks are expected to become a key enabler for interconnecting quantum devices. In contrast to classical communication networks, however, information transfer in quantum networks is usually restricted to short distances due to physical constraints of entanglement distribution. Satellites can extend entanglement distribution over long distances, but routing in such networks is challenging because satellite motion and stochastic link generation create a highly dynamic quantum topology. Existing routing methods often rely on global topology information that quickly becomes outdated due to delays in the classical control plane, while decentralized methods typically act on incomplete local information. We propose SatQNet, a reinforcement learning approach for entanglement routing in satellite-assisted quantum networks that can be decentralized at runtime. Its key innovation is an edge-centric directed line graph neural network that performs local message passing on directed edge embeddings, enabling it to better capture link properties in high-degree and time-varying topologies. By exchanging messages with neighboring repeaters, SatQNet learns a local graph representation at runtime that supports agents in establishing high-fidelity end-to-end entanglements. Trained on random graphs, SatQNet outperforms heuristic and learning-based approaches across diverse settings, including a real-world European backbone topology, and generalizes to unseen topologies without retraining.

Problem

• quantum network
• entanglement routing
• satellite-assisted
• dynamic topology
• decentralized routing

Innovation

• Satellite-assisted quantum network
• Entanglement routing
• Directed line graph neural network
• Decentralized reinforcement learning
• Dynamic quantum topology