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.
@article{arxiv.2501.08418,
title = {CVaR-Based Variational Quantum Optimization for User Association in Handoff-Aware Vehicular Networks},
author = {Zijiang Yan and Hao Zhou and Jianhua Pei and Aryan Kaushik and Hina Tabassum and Ping Wang},
journal= {arXiv preprint arXiv:2501.08418},
year = {2025}
}
Comments
Accepted in IEEE International Conference on Communications (ICC 2025)