D2D Power Allocation via Quantum Graph Neural Network
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arXiv
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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
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| _version_ | 1866918214460506112 |
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| author | Le, Tung Giang Nguyen, Xuan Tung Hwang, Won-Joo |
| author_facet | Le, Tung Giang Nguyen, Xuan Tung Hwang, Won-Joo |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_15246 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | D2D Power Allocation via Quantum Graph Neural Network Le, Tung Giang Nguyen, Xuan Tung Hwang, Won-Joo Machine Learning 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. |
| title | D2D Power Allocation via Quantum Graph Neural Network |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2511.15246 |