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.
@article{arxiv.2511.15246,
title = {D2D Power Allocation via Quantum Graph Neural Network},
author = {Tung Giang Le and Xuan Tung Nguyen and Won-Joo Hwang},
journal= {arXiv preprint arXiv:2511.15246},
year = {2025}
}