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

TL;DR

This paper introduces a scalable quantum message passing graph neural network (SQM-GNN) that leverages quantum computing to improve wireless network management, demonstrating superior performance on a device-to-device power control task.

Contribution

The paper proposes a novel SQM-GNN architecture that decomposes graphs into subgraphs and applies shared PQCs, enhancing scalability and efficiency in quantum GNNs for wireless applications.

Findings

SQM-GNN outperforms classical GNNs and heuristics in D2D power control.

The architecture effectively incorporates node and edge features.

Demonstrates potential of quantum GNNs for future wireless network optimization.

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.