CVaR-Based Variational Quantum Optimization for User Association in Handoff-Aware Vehicular Networks
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arXiv
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| Hauptverfasser: | , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866929698645213184 |
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| author | Yan, Zijiang Zhou, Hao Pei, Jianhua Kaushik, Aryan Tabassum, Hina Wang, Ping |
| author_facet | Yan, Zijiang Zhou, Hao Pei, Jianhua Kaushik, Aryan Tabassum, Hina Wang, Ping |
| contents | Efficient resource allocation is essential for optimizing various tasks in wireless networks, which are usually formulated as generalized assignment problems (GAP). GAP, as a generalized version of the linear sum assignment problem, involves both equality and inequality constraints that add computational challenges. In this work, we present a novel Conditional Value at Risk (CVaR)-based Variational Quantum Eigensolver (VQE) framework to address GAP in vehicular networks (VNets). Our approach leverages a hybrid quantum-classical structure, integrating a tailored cost function that balances both objective and constraint-specific penalties to improve solution quality and stability. Using the CVaR-VQE model, we handle the GAP efficiently by focusing optimization on the lower tail of the solution space, enhancing both convergence and resilience on noisy intermediate-scale quantum (NISQ) devices. We apply this framework to a user-association problem in VNets, where our method achieves 23.5% improvement compared to the deep neural network (DNN) approach. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_08418 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | CVaR-Based Variational Quantum Optimization for User Association in Handoff-Aware Vehicular Networks Yan, Zijiang Zhou, Hao Pei, Jianhua Kaushik, Aryan Tabassum, Hina Wang, Ping Machine Learning Artificial Intelligence Networking and Internet Architecture Systems and Control Efficient resource allocation is essential for optimizing various tasks in wireless networks, which are usually formulated as generalized assignment problems (GAP). GAP, as a generalized version of the linear sum assignment problem, involves both equality and inequality constraints that add computational challenges. In this work, we present a novel Conditional Value at Risk (CVaR)-based Variational Quantum Eigensolver (VQE) framework to address GAP in vehicular networks (VNets). Our approach leverages a hybrid quantum-classical structure, integrating a tailored cost function that balances both objective and constraint-specific penalties to improve solution quality and stability. Using the CVaR-VQE model, we handle the GAP efficiently by focusing optimization on the lower tail of the solution space, enhancing both convergence and resilience on noisy intermediate-scale quantum (NISQ) devices. We apply this framework to a user-association problem in VNets, where our method achieves 23.5% improvement compared to the deep neural network (DNN) approach. |
| title | CVaR-Based Variational Quantum Optimization for User Association in Handoff-Aware Vehicular Networks |
| topic | Machine Learning Artificial Intelligence Networking and Internet Architecture Systems and Control |
| url | https://arxiv.org/abs/2501.08418 |