D2D Power Allocation via Quantum Graph Neural Network
This work addresses computational bottlenecks in large-scale wireless optimization, though it is incremental as it builds on existing quantum and GNN methods.
The paper tackled the problem of scalable resource management in wireless networks by proposing a quantum Graph Neural Network (QGNN) that matches classical performance in D2D power control for SINR maximization, using fewer parameters and inherent parallelism.
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.