Quantum Graph Neural Network for Resource Management in Wireless Communication

Effective resource management in wireless communication systems is a critical requirement, given the interference challenges and the need for efficient allocation strategies. Graph Neural Networks (GNNs) have emerged as powerful tools for tackling this problem due to their scalability and ability to generalize over complex graph-structured data, like that found in wireless networks. However, the computational complexity of GNNs on large-scale systems limits their real-time deployment. Recent advances in quantum computing and quantum machine learning (QML) offer a promising solution by leveraging the unique properties of quantum systems to reduce computational overhead. This paper proposes a Quantum Graph Neural Network (QGNN) model specifically designed for a wireless communication scenario, implemented as a variational quantum circuit (VQC) to realize the message-passing mechanism integral to GNNs. Applied to a supervised resource management task in a wireless network, the proposed QGNN demonstrates encouraging results, showcasing its potential as a scalable and efficient approach for real-time resource management in future wireless systems.