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

arXiv: https://arxiv.org/abs/2501.08418 · HTML: https://mlsanity.com/paper/2501.08418/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 15 references that are in the map.
- First posted on arXiv 14 Jan 2025.

## Builds on (most cited first, 7 of 7)

- [The power of quantum neural networks](https://mlsanity.com/paper/2011.00027/) (2020)
- [Reinforcement Learning for Joint V2I Network Selection and Autonomous Driving Policies](https://mlsanity.com/paper/2208.02249/) (2022)
- [Generalized Multi-Objective Reinforcement Learning with Envelope Updates in URLLC-enabled Vehicular Networks](https://mlsanity.com/paper/2405.11331/) (2024)
- [Deep Unsupervised Learning for Generalized Assignment Problems: A Case-Study of User-Association in Wireless Networks](https://mlsanity.com/paper/2103.14548/) (2021)
- [Mobility-Aware Performance in Hybrid RF and Terahertz Wireless Networks](https://mlsanity.com/paper/2112.10249/) (2021)
- [Joint Antenna Selection and Beamforming for Massive MIMO-enabled Over-the-Air Federated Learning](https://mlsanity.com/paper/2305.17022/) (2023)
- [Optimizing Vehicular Networks with Variational Quantum Circuits-based Reinforcement Learning](https://mlsanity.com/paper/2405.18984/) (2024)

## Built on by (most cited first, 2 of 2)

- [Hierarchical LLM-Driven Control for HAPS-Assisted UAV Networks: Joint Optimization of Flight and Connectivity](https://mlsanity.com/paper/2605.11509/) (2026)
- [Intelligent Multi-UAV Navigation in ITNTNs: A Hierarchical LLM Approach](https://mlsanity.com/paper/2607.18604/) (2026)

## Abstract (arXiv)

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.