D2D Power Allocation via Quantum Graph Neural Network

2025/11/19 by Le, Tung Giang, Nguyen, Xuan Tung, Hwang, Won-Joo
Computer Science · #FOS: Computer and information sciences #Machine Learning (cs.LG) #Quantum Computing Algorithms and Architecture #Quantum Information and Cryptography #Quantum-Dot Cellular Automata

paper · doi:10.48550/arxiv.2511.15246

openalex publication_date 2025/11/19 · openalex created_date 2025/11/23 · openalex updated_date 2026/07/28

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.

Citations

• Joint Access Point Activation and Power Allocation for Cell-Free Massive MIMO Aided ISAC Systems
• Hybrid Quantum Convolutional Neural Network-Aided Pilot Assignment in Cell-Free Massive MIMO Systems
• Federated Domain Generalization with Data-free On-server Matching Gradient
• Graph Neural Networks for Next-Generation-IoT: Recent Advances and Open Challenges
• Jointly Optimizing Power Allocation and Device Association for Robust IoT Networks under Infeasible Circumstances
• Distortion Resilience for Goal-Oriented Semantic Communication
• Revisiting LARS for Large Batch Training Generalization of Neural Networks
• HCFL: A High Compression Approach for Communication-Efficient Federated Learning in Very Large Scale IoT Networks
• Graph Neural Networks for Scalable Radio Resource Management: Architecture Design and Theoretical Analysis

Related

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• Novel Architecture of Parameterized Quantum Circuit for Graph Convolutional Network
• Edge-Local and Qubit-Efficient Quantum Graph Learning for the NISQ Era
• Quantum Computing for Large-scale Network Optimization: Opportunities and Challenges