Scalable Quantum Message Passing Graph Neural Networks for Next-Generation Wireless Communications: Architectures, Use Cases, and Future Directions

2026-01-26

AI Summary

This work addresses the computational inefficiency of classical graph neural networks (GNNs) in large-scale dense wireless networks and the limited scalability of existing purely quantum message-passing models due to qubit constraints. To overcome these challenges, the authors propose the Scalable Quantum Message-passing GNN (SQM-GNN), which decomposes a large graph into subgraphs and employs shared parameterized quantum circuits across subgraphs to circumvent hardware limitations. By integrating both node and edge features, SQM-GNN fully captures the structural characteristics of wireless networks. The model adopts a hybrid quantum-classical architecture that balances expressive power with computational efficiency. Evaluated on device-to-device (D2D) power control tasks, SQM-GNN significantly outperforms classical GNNs and heuristic baselines, demonstrating its practical potential for wireless resource management.

Technology Category

• Machine Learning: Quantum Machine Learning
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• 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
• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines

Abstract

Graph Neural Networks (GNNs) are eminently suitable for wireless resource management, thanks to their scalability, but they still face computational challenges in large-scale, dense networks in classical computers. The integration of quantum computing with GNNs offers a promising pathway for enhancing computational efficiency because they reduce the model complexity. This is achieved by leveraging the quantum advantages of parameterized quantum circuits (PQCs), while retaining the expressive power of GNNs. However, existing pure quantum message passing models remain constrained by the limited number of qubits, hence limiting the scalability of their application to the wireless systems. As a remedy, we conceive a Scalable Quantum Message Passing Graph Neural Network (SQM-GNN) relying on a quantum message passing architecture. To address the aforementioned scalability issue, we decompose the graph into subgraphs and apply a shared PQC to each local subgraph. Importantly, the model incorporates both node and edge features, facilitating the full representation of the underlying wireless graph structure. We demonstrate the efficiency of SQM GNN on a device-to-device (D2D) power control task, where it outperforms both classical GNNs and heuristic baselines. These results highlight SQM-GNN as a promising direction for future wireless network optimization.

Problem

• Graph Neural Networks
• Quantum Computing
• Scalability
• Wireless Communications
• Message Passing

Innovation

• Scalable Quantum Message Passing
• Parameterized Quantum Circuits
• Graph Neural Networks
• Wireless Resource Management
• Subgraph Decomposition