Hybrid Quantum-Classical Maximum-Likelihood Detection via Grover-based Adaptive Search for RIS-assisted Broadband Wireless Systems
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arXiv
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| Auteurs principaux: | , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866913827101081600 |
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| author | Tariq, Maryam Abdelrahim, Raneem Alhussein, Omar Muhaidat, Sami |
| author_facet | Tariq, Maryam Abdelrahim, Raneem Alhussein, Omar Muhaidat, Sami |
| contents | The escalating complexity and stringent performance demands of sixth-generation wireless systems necessitate advanced signal processing methods capable of simultaneously achieving high spectral efficiency and low computational complexity, especially under frequency-selective propagation conditions. In this paper, we propose a hybrid quantum-classical detection framework for broadband systems enhanced by reconfigurable intelligent surfaces (RISs). We address the maximum likelihood detection (MLD) problem for RIS-aided broadband wireless communications by formulating it as a quadratic unconstrained binary optimization problem, that is then solved using Grover adaptive search (GAS). To accelerate convergence, we initialize the GAS algorithm with a threshold based on a classical minimum mean-squared error detector. The simulation results show that the proposed hybrid classical-quantum detection scheme achieves near-optimal MLD performance while substantially reducing query complexity. These findings highlight the potential of quantum-enhanced detection strategies combined with RIS technology, offering efficient and near-optimal solutions for broadband wireless communications. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_03914 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Hybrid Quantum-Classical Maximum-Likelihood Detection via Grover-based Adaptive Search for RIS-assisted Broadband Wireless Systems Tariq, Maryam Abdelrahim, Raneem Alhussein, Omar Muhaidat, Sami Signal Processing Networking and Internet Architecture The escalating complexity and stringent performance demands of sixth-generation wireless systems necessitate advanced signal processing methods capable of simultaneously achieving high spectral efficiency and low computational complexity, especially under frequency-selective propagation conditions. In this paper, we propose a hybrid quantum-classical detection framework for broadband systems enhanced by reconfigurable intelligent surfaces (RISs). We address the maximum likelihood detection (MLD) problem for RIS-aided broadband wireless communications by formulating it as a quadratic unconstrained binary optimization problem, that is then solved using Grover adaptive search (GAS). To accelerate convergence, we initialize the GAS algorithm with a threshold based on a classical minimum mean-squared error detector. The simulation results show that the proposed hybrid classical-quantum detection scheme achieves near-optimal MLD performance while substantially reducing query complexity. These findings highlight the potential of quantum-enhanced detection strategies combined with RIS technology, offering efficient and near-optimal solutions for broadband wireless communications. |
| title | Hybrid Quantum-Classical Maximum-Likelihood Detection via Grover-based Adaptive Search for RIS-assisted Broadband Wireless Systems |
| topic | Signal Processing Networking and Internet Architecture |
| url | https://arxiv.org/abs/2505.03914 |