CVaR-Based Variational Quantum Optimization for User Association in Handoff-Aware Vehicular Networks

• URL: http://arxiv.org/abs/2501.08418v2
• Date: Tue, 04 Feb 2025 19:51:53 GMT
• Title: CVaR-Based Variational Quantum Optimization for User Association in Handoff-Aware Vehicular Networks
• Authors: Zijiang Yan, Hao Zhou, Jianhua Pei, Aryan Kaushik, Hina Tabassum, Ping Wang,
• Abstract summary: We present a novel Conditional Value at Risk (CVaR)-based Variational Quantum Eigensolver (VQE) framework to address generalized assignment problems (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. 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.
• Score: 23.140655547353994
• License:
• Abstract: 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.

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