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

2025-01-14

AI Summary

This paper addresses the joint optimization of user association and base station handover in vehicular networks, modeling resource allocation—subject to equality and inequality constraints—as a generalized assignment problem. Method: We propose the first constrained-optimization-oriented hybrid quantum algorithm, CVaR-VQE, which leverages Conditional Value-at-Risk (CVaR) to concentrate optimization on the low-tail region of the solution distribution, thereby enhancing convergence and robustness on Noisy Intermediate-Scale Quantum (NISQ) devices. A custom cost function jointly encodes the objective and hard constraints, supported by lightweight constraint encoding and an adaptive quantum circuit architecture. Results: Experiments demonstrate that our approach improves solution quality by 23.5% over state-of-the-art deep neural networks for user association, while significantly enhancing solution stability and feasibility guarantee capability.

Technology Category

• Constraint Satisfaction and Optimization: Constraint Optimization
• Search and Optimization: Mixed Discrete/Continuous Search
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

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.

Problem

• Resource Allocation
• Handover Optimization
• Vehicular Networks

Innovation

• Quantum-Classical Hybrid Method
• Resource Allocation Optimization
• Noise Resilience in Vehicular Networks