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

arXiv: https://arxiv.org/abs/2604.09306 · HTML: https://mlsanity.com/paper/2604.09306/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 73 references that are in the map.
- First posted on arXiv 10 Apr 2026.

## Builds on (most cited first, 22 of 22)

- [Line Graph Neural Networks for Link Prediction](https://mlsanity.com/paper/2010.10046/) (2020)
- [Deep Reinforcement Learning meets Graph Neural Networks: exploring a routing optimization use case](https://mlsanity.com/paper/1910.07421/) (2019)
- [RouteNet: Leveraging Graph Neural Networks for network modeling and optimization in SDN](https://mlsanity.com/paper/1910.01508/) (2019)
- [Reinforcement Learning with Non-Markovian Rewards](https://mlsanity.com/paper/1912.02552/) (2019)
- [Towards Principled Graph Transformers](https://mlsanity.com/paper/2401.10119/) (2024)
- [Towards Generalizability of Multi-Agent Reinforcement Learning in Graphs with Recurrent Message Passing](https://mlsanity.com/paper/2402.05027/) (2024)
- [Distributed Routing in a Quantum Internet](https://mlsanity.com/paper/1907.11630/) (2019)
- [Fidelity-Guarantee Entanglement Routing in Quantum Networks](https://mlsanity.com/paper/2111.07764/) (2021)
- [Quantum Internet Protocol Stack: a Comprehensive Survey](https://mlsanity.com/paper/2202.10894/) (2022)
- [Physics-Informed Quantum Communication Networks: A Vision Towards the Quantum Internet](https://mlsanity.com/paper/2204.09233/) (2022)
- [Multi-Entanglement Routing Design over Quantum Networks](https://mlsanity.com/paper/2205.15501/) (2022)
- [Efficient Reinforcement Learning in Probabilistic Reward Machines](https://mlsanity.com/paper/2408.10381/) (2024)
- [Shortcuts to quantum network routing](https://mlsanity.com/paper/1610.05238/) (2016)
- [Quantum Internet- Applications, Functionalities, Enabling Technologies, Challenges, and Research Directions](https://mlsanity.com/paper/2101.04427/) (2021)
- [Improving Graph Neural Networks by Learning Continuous Edge Directions](https://mlsanity.com/paper/2410.14109/) (2024)
- [A Multiple-Entanglement Routing Framework for Quantum Networks](https://mlsanity.com/paper/2207.11817/) (2022)
- [A Quantum Overlay Network for Efficient Entanglement Distribution](https://mlsanity.com/paper/2212.01694/) (2022)
- [FENDI: Toward High-Fidelity Entanglement Distribution in the Quantum Internet](https://mlsanity.com/paper/2301.08269/) (2023)
- [RELiQ: Scalable Entanglement Routing via Reinforcement Learning in Quantum Networks](https://mlsanity.com/paper/2511.22321/) (2025)
- [Temporal Modes of Light in Satellite-to-Earth Quantum Communications](https://mlsanity.com/paper/2106.13693/) (2021)
- [Entanglement Distribution in Satellite-based Dynamic Quantum Networks](https://mlsanity.com/paper/2306.08894/) (2023)
- [QuESat: Satellite-Assisted Quantum Internet for Global-Scale Entanglement Distribution](https://mlsanity.com/paper/2501.15376/) (2025)

## Built on by (most cited first, 1 of 1)

- [When AI meets quantum information: A comprehensive review](https://mlsanity.com/paper/2607.00365/) (2026)

## Abstract (arXiv)

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.