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HQN-RL: Hybrid Quantum-Neuromorphic Reinforcement Learning for Adaptive Intelligent Control of Automated Vehicles

Mazyar Taghavi, Javad Vahidi

Published Dec 19, 2025• 0 citations
Publisher

Abstract

This paper presents a hybrid quantum neuromorphic reinforcement learning framework for adaptive intelligent control of automated vehicles operating in uncertain and heterogeneous environments. The proposed architecture integrates quantum sampling for exploration with neuromorphic computing for real-time, energy-efficient policy execution, enabling rapid convergence and robust behavior under nonstationary driving conditions. A unified theoretical analysis is provided, covering hybrid optimization convergence, controller stability, and computational complexity. The framework is evaluated through largescale simulations, quantum hardware experiments, neuromorphic processor deployment, and a sim-to-real study involving five autonomous vehicles tested in a controlled environment at the Intelligent Knowledge City research facility in Isfahan, Iran. Results demonstrate improved sample efficiency, reduced energy consumption, and enhanced robustness compared with classical reinforcement learning baselines. Furthermore, the hybrid design maintains real-time feasibility and scalability, showing promise for next-generation autonomous driving systems requiring adaptive decision-making under limited computational resources. All source code and implementation details for reproduction and further research are publicly available in the project repository.

DOI
10.36227/techrxiv.176617710.05274660/v1

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