# D2D Power Allocation via Quantum Graph Neural Network

arXiv: https://arxiv.org/abs/2511.15246 · HTML: https://mlsanity.com/paper/2511.15246/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 15 references that are in the map.
- First posted on arXiv 19 Nov 2025.

## Builds on (most cited first, 7 of 7)

- [Graph Neural Networks for Scalable Radio Resource Management: Architecture Design and Theoretical Analysis](https://mlsanity.com/paper/2007.07632/) (2020)
- [HCFL: A High Compression Approach for Communication-Efficient Federated Learning in Very Large Scale IoT Networks](https://mlsanity.com/paper/2204.06760/) (2022)
- [Federated Domain Generalization with Data-free On-server Matching Gradient](https://mlsanity.com/paper/2501.14653/) (2025)
- [Revisiting LARS for Large Batch Training Generalization of Neural Networks](https://mlsanity.com/paper/2309.14053/) (2023)
- [Distortion Resilience for Goal-Oriented Semantic Communication](https://mlsanity.com/paper/2309.14587/) (2023)
- [Hybrid Quantum Convolutional Neural Network-Aided Pilot Assignment in Cell-Free Massive MIMO Systems](https://mlsanity.com/paper/2507.06585/) (2025)
- [Joint Access Point Activation and Power Allocation for Cell-Free Massive MIMO Aided ISAC Systems](https://mlsanity.com/paper/2507.09425/) (2025)

## Abstract (arXiv)

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.