Graph Neural Networks (GNNs) are eminently suitable for wireless resource management, thanks to their scalability, but they still face computational challenges in large-scale, dense networks in classical computers. The integration of quantum computing with GNNs offers a promising pathway for enhancing computational efficiency because they reduce the model complexity. This is achieved by leveraging the quantum advantages of parameterized quantum circuits (PQCs), while retaining the expressive power of GNNs. However, existing pure quantum message passing models remain constrained by the limited number of qubits, hence limiting the scalability of their application to the wireless systems. As a remedy, we conceive a Scalable Quantum Message Passing Graph Neural Network (SQM-GNN) relying on a quantum message passing architecture. To address the aforementioned scalability issue, we decompose the graph into subgraphs and apply a shared PQC to each local subgraph. Importantly, the model incorporates both node and edge features, facilitating the full representation of the underlying wireless graph structure. We demonstrate the efficiency of SQM GNN on a device-to-device (D2D) power control task, where it outperforms both classical GNNs and heuristic baselines. These results highlight SQM-GNN as a promising direction for future wireless network optimization.

翻译：图神经网络（GNNs）凭借其可扩展性，非常适合用于无线资源管理，但在经典计算机的大规模密集网络中仍面临计算挑战。将量子计算与GNN相结合，为提升计算效率提供了一条有前景的路径，因为它们能够降低模型复杂度。这是通过利用参数化量子电路（PQCs）的量子优势，同时保留GNN的表达能力来实现的。然而，现有的纯量子消息传递模型仍受限于量子比特数量有限，从而限制了其在无线系统中应用的可扩展性。为此，我们提出了一种基于量子消息传递架构的可扩展量子消息传递图神经网络（SQM-GNN）。为解决上述可扩展性问题，我们将图分解为子图，并对每个局部子图应用共享的PQC。重要的是，该模型同时包含了节点和边特征，从而能够完整地表征底层无线图结构。我们在设备到设备（D2D）功率控制任务中验证了SQM-GNN的效率，其性能超越了经典GNN和启发式基线方法。这些结果凸显了SQM-GNN作为未来无线网络优化的一个有前景的研究方向。