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| Main Author: | |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2511.03717 |
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| _version_ | 1866908631402807296 |
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| author | Ahmed, Shakil |
| author_facet | Ahmed, Shakil |
| contents | This paper presents a noise-aware quantum-assisted framework for blockage prediction in reconfigurable intelligent surface (RIS)-enabled wireless networks. The proposed architecture integrates a Quantum Base Station (QBS), a Quantum RIS (QRIS), and a mobile Quantum User Node (QUN). Visual information captured by an onboard RGB camera is amplitude-encoded into quantum states, while channel state observations are mapped into quantum rotation-encoded features. These hybrid inputs are processed through variational quantum circuits, enabling ternary classification of the link status. To address the inherent imperfections of noisy intermediate-scale quantum (NISQ) hardware, the system explicitly models depolarizing and dephasing channels along direct and QRIS-assisted paths. A fidelity-aware training objective is employed to jointly minimize classification loss and quantum state degradation, with amplitude damping and synthetic noise injection enhancing robustness. Simulation results on a quantum-adapted version of the ViWi dataset demonstrate that the proposed hybrid quantum model achieves superior accuracy and stability under realistic noise conditions, outperforming baseline and single-modality approaches. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_03717 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Quantum Noise-Aware RIS-Aided Wireless Networks Using Variational Encoding and Signal Stabilization Ahmed, Shakil Quantum Algebra This paper presents a noise-aware quantum-assisted framework for blockage prediction in reconfigurable intelligent surface (RIS)-enabled wireless networks. The proposed architecture integrates a Quantum Base Station (QBS), a Quantum RIS (QRIS), and a mobile Quantum User Node (QUN). Visual information captured by an onboard RGB camera is amplitude-encoded into quantum states, while channel state observations are mapped into quantum rotation-encoded features. These hybrid inputs are processed through variational quantum circuits, enabling ternary classification of the link status. To address the inherent imperfections of noisy intermediate-scale quantum (NISQ) hardware, the system explicitly models depolarizing and dephasing channels along direct and QRIS-assisted paths. A fidelity-aware training objective is employed to jointly minimize classification loss and quantum state degradation, with amplitude damping and synthetic noise injection enhancing robustness. Simulation results on a quantum-adapted version of the ViWi dataset demonstrate that the proposed hybrid quantum model achieves superior accuracy and stability under realistic noise conditions, outperforming baseline and single-modality approaches. |
| title | Quantum Noise-Aware RIS-Aided Wireless Networks Using Variational Encoding and Signal Stabilization |
| topic | Quantum Algebra |
| url | https://arxiv.org/abs/2511.03717 |