mlsanityData to 14 Oct 2026

Optimizing Vehicular Networks with Variational Quantum Circuits-based Reinforcement Learning

arXiv 2405.18984 ↗First posted 29 May 2024cs.LG cs.AI cs.NIcited by 1

On the map: Deep reinforcement learning methods → Quantum reinforcement learning

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.

Key facts. Cited by 1 paper 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.

Builds on

3 of 5 references.

References · 3

  1. 2022Reinforcement Learning for Joint V2I Network Selection and Autonomous Driving Policiescited by 7
  2. 2023Multi-UAV Speed Control with Collision Avoidance and Handover-aware Cell Association: DRL with Action Branchingcited by 5
  3. 2021Mobility-Aware Performance in Hybrid RF and Terahertz Wireless Networkscited by 4

Built on by

Cited by year

Citing papers per year: 2025: 1.

1 paper.

  1. 2025CVaR-Based Variational Quantum Optimization for User Association in Handoff-Aware Vehicular Networkscited by 2

Similar, not cited

Close in wording, no citation either way

  1. 2025Lyapunov-Aware Quantum-Inspired Reinforcement Learning for Continuous-Time Vehicle Control: A Feasibility Study
  2. 2024Architectural Influence on Variational Quantum Circuits in Multi-Agent Reinforcement Learning: Evolutionary Strategies for Optimization
  3. 2024Generalized Multi-Objective Reinforcement Learning with Envelope Updates in URLLC-enabled Vehicular Networks
  4. 2026Hybrid Quantum Reinforcement Learning with QAOA for Improved Vehicle Routing Optimization
  5. 2026Quantum Reinforcement Learning with Transformers for the Capacitated Vehicle Routing Problem
  6. 2023Nav-Q: Quantum Deep Reinforcement Learning for Collision-Free Navigation of Self-Driving Cars
  7. 2024A Study on Optimization Techniques for Variational Quantum Circuits in Reinforcement Learning
  8. 2024VQC-Based Reinforcement Learning with Data Re-uploading: Performance and Trainability