用量子算法优化车载网络用户关联，提升资源分配效率
CVaR-Based Variational Quantum Optimization for User Association in Handoff-Aware Vehicular Networks
- 基于CVaR的量子变分算法，融合目标与约束惩罚函数
- 在车载网络中比深度神经网络提升23.5%性能
- 适合需要高稳定性的量子优化场景，尤其适用于噪声设备
无线网络中的高效资源分配对优化各类任务至关重要，通常被建模为广义分配问题（GAP）。GAP作为线性和分配问题的推广形式，包含等式与不等式约束，带来显著计算挑战。本文提出一种基于条件风险价值（CVaR）的变分量子特征值求解器（VQE）框架，用于解决车载网络（VNets）中的GAP问题。该方法采用混合量子-经典架构，设计定制化代价函数以平衡目标与约束惩罚，提升解的质量与稳定性。通过聚焦解空间下尾部的优化，所提CVaR-VQE模型在噪声中等规模量子（NISQ）设备上表现出更强的收敛性与鲁棒性。我们将该框架应用于车载网络中的用户关联问题，实验显示其相比深度神经网络（DNN）方法性能提升23.5%。
原文摘要 · Abstract (English)
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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