  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.
