D2D Power Allocation via Quantum Graph Neural Network

TL;DR

This paper introduces a quantum graph neural network (QGNN) for device-to-device power control in wireless networks, leveraging quantum circuits to improve scalability and efficiency while maintaining classical performance.

Contribution

It presents a novel fully quantum GNN architecture using parameterized quantum circuits for message passing, enabling quantum-accelerated wireless resource management.

Findings

QGNN matches classical GNN performance in D2D power control.

QGNN uses fewer parameters than classical counterparts.

QGNN demonstrates inherent parallelism and scalability.

Abstract

Increasing wireless network complexity demands scalable resource management. Classical GNNs excel at graph learning but incur high computational costs in large-scale settings. We present a fully quantum Graph Neural Network (QGNN) that implements message passing via Parameterized Quantum Circuits (PQCs). Our Quantum Graph Convolutional Layers (QGCLs) encode features into quantum states, process graphs with NISQ-compatible unitaries, and retrieve embeddings through measurement. Applied to D2D power control for SINR maximization, our QGNN matches classical performance with fewer parameters and inherent parallelism. This end-to-end PQC-based GNN marks a step toward quantum-accelerated wireless optimization.