Optimizing Vehicular Networks with Variational Quantum Circuits-based Reinforcement Learning
<|reference_start|>Optimizing Vehicular Networks with Variational Quantum Circuits-based Reinforcement Learning: 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.<|reference_end|>
@article{yan2024optimizing,
    title={Optimizing Vehicular Networks with Variational Quantum Circuits-based
  Reinforcement Learning},
    author={Zijiang Yan, Ramsundar Tanikella, Hina Tabassum},
    journal={arXiv preprint arXiv:2405.18984},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.18984},
    primaryClass={cs.LG cs.AI cs.NI}
}