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

TL;DR

This paper introduces a CVaR-based variational quantum optimization method tailored for resource allocation in vehicular networks, improving solution quality and robustness on noisy quantum devices.

Contribution

It presents a novel hybrid quantum-classical CVaR-VQE framework specifically designed for generalized assignment problems in vehicular networks, with enhanced convergence and stability.

Findings

Achieves 23.5% improvement over DNN methods

Handles constraints effectively on NISQ devices

Focuses optimization on the lower tail of solution space

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.