# Optimizing Vehicular Networks with Variational Quantum Circuits-based Reinforcement Learning

arXiv: https://arxiv.org/abs/2405.18984 · HTML: https://mlsanity.com/paper/2405.18984/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in Quantum reinforcement learning, one of 15 directions in Deep reinforcement learning methods.
- Builds on 3 of its 5 references that are in the map.
- First posted on arXiv 29 May 2024.

## Placed in

- Deep reinforcement learning methods / [Quantum reinforcement learning](https://mlsanity.com/deeprl/quantum-reinforcement-learning/)

## Builds on (most cited first, 3 of 3)

- [Reinforcement Learning for Joint V2I Network Selection and Autonomous Driving Policies](https://mlsanity.com/paper/2208.02249/) (2022)
- [Multi-UAV Speed Control with Collision Avoidance and Handover-aware Cell Association: DRL with Action Branching](https://mlsanity.com/paper/2307.13158/) (2023)
- [Mobility-Aware Performance in Hybrid RF and Terahertz Wireless Networks](https://mlsanity.com/paper/2112.10249/) (2021)

## Built on by (most cited first, 1 of 1)

- [CVaR-Based Variational Quantum Optimization for User Association in Handoff-Aware Vehicular Networks](https://mlsanity.com/paper/2501.08418/) (2025)

## Abstract (arXiv)

In vehicular networks (VNets), ensuring both road safety and dependable network connectivity is of utmost importance. Achieving this necessitates the creation of resilient and efficient decision-making policies that prioritize multiple objectives. In this paper, we develop a Variational Quantum Circuit (VQC)-based multi-objective reinforcement learning (MORL) framework to characterize efficient network selection and autonomous driving policies in a vehicular network (VNet). Numerical results showcase notable enhancements in both convergence rates and rewards when compared to conventional deep-Q networks (DQNs), validating the efficacy of the VQC-MORL solution.