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

Corresponding Author:[email protected]

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Ramsundar Tanikella
Hina Tabassum
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Abstract

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