Topology-Aware Quantum Graph Neural Networks for Sum-Rate Maximization in Fluid Antenna Systems

This paper presents a quantum graph-based solution that leverages a quantum circuit to improve learning efficiency, towards maximizing the sum-rate of wireless communication with fluid antennas in dynamic environments. The employed quantum graph neural networks (QGNN) consists of three main blocks, including (i) a quantum encoding layer, (ii) a quantum graph neural network layer, and (iii) an optimizer layer, which collectively comprise the end-to-end learning workflow. The QGNN adjusts parameters through a quantum graph neural network layer, utilizing basic linear gates on a parameterized quantum circuit (PQC) platform. Additionally, the QGNN circuit is designed with shallow depth and optimized gate composition to reduce quantum resource usage and accelerate convergence during training. The results demonstrate that the proposed QGNN offers competitive performance relative to the existing PQC model. Furthermore, this paper highlights the versatility of quantum graph-based solutions for addressing dynamic, topology-aware wireless network problems.