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arXiv · 2511.15246

D2D Power Allocation via Quantum Graph Neural Network

Tung Giang Le·Xuan Tung Nguyen·Won-Joo Hwang
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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.

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Tung Giang Le, Xuan Tung Nguyen, Won-Joo Hwang. 2025-11-19. D2D Power Allocation via Quantum Graph Neural Network. https://doi.org/10.23919/icmu65253.2025.11219153

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