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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2604.10453 |
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| _version_ | 1866918440890007552 |
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| author | Hassan, Noha Fernando, Xavier Yanikomeroglu, Halim |
| author_facet | Hassan, Noha Fernando, Xavier Yanikomeroglu, Halim |
| contents | As a key enabler for sixth-generation (6G) wireless communications, reconfigurable intelligent surfaces (RISs) provide the flexibility to control signal strength. Nevertheless, optimizing hundreds of elements is computationally expensive. To overcome this challenge, we present a quantum framework (QGCN) to jointly optimize the physical and electromagnetic response of a double-sided RIS design that incorporates discrete phase shifts and inter-element coupling. The core contribution is the adaptive activation or deactivation of elements, allowing a virtual spacing mechanism using PIN diode switches. We then solve a multi-objective problem that maximizes the minimum user data rate subject to constraints on aperture length and mutual coupling between active elements. Experimental results on IBM Quantum's 127-qubit ibm_kyiv superconducting processor demonstrate that the proposed QGCN algorithm reduces both per-iteration computational complexity and memory requirements compared to existing approaches. Also, the QGCN outperforms classical graph neural networks (GNN) on an equivalent graph topology by an additional $+$0.38 bps/Hz. This advantage is increasing with increasing array sizes. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_10453 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Quantum Graph Neural Networks for Double-Sided Reconfigurable Intelligent Surface Optimization Hassan, Noha Fernando, Xavier Yanikomeroglu, Halim Systems and Control As a key enabler for sixth-generation (6G) wireless communications, reconfigurable intelligent surfaces (RISs) provide the flexibility to control signal strength. Nevertheless, optimizing hundreds of elements is computationally expensive. To overcome this challenge, we present a quantum framework (QGCN) to jointly optimize the physical and electromagnetic response of a double-sided RIS design that incorporates discrete phase shifts and inter-element coupling. The core contribution is the adaptive activation or deactivation of elements, allowing a virtual spacing mechanism using PIN diode switches. We then solve a multi-objective problem that maximizes the minimum user data rate subject to constraints on aperture length and mutual coupling between active elements. Experimental results on IBM Quantum's 127-qubit ibm_kyiv superconducting processor demonstrate that the proposed QGCN algorithm reduces both per-iteration computational complexity and memory requirements compared to existing approaches. Also, the QGCN outperforms classical graph neural networks (GNN) on an equivalent graph topology by an additional $+$0.38 bps/Hz. This advantage is increasing with increasing array sizes. |
| title | Quantum Graph Neural Networks for Double-Sided Reconfigurable Intelligent Surface Optimization |
| topic | Systems and Control |
| url | https://arxiv.org/abs/2604.10453 |