loading page

Optimizing Vehicular Networks with Variational Quantum Circuits-based Reinforcement Learning
  • Zijiang Yan,
  • Ramsundar Tanikella,
  • Hina Tabassum
Zijiang Yan

Corresponding Author:[email protected]

Author Profile
Ramsundar Tanikella
Hina Tabassum
Author Profile


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.

29 May 2024Submitted to TechRxiv
04 Jun 2024Published in TechRxiv