Quantum Reinforcement Learning for Data-Driven Decision-Making in Autonomous Systems

Quantum Reinforcement Learning (QRL) is a cutting-edge field combining quantum computing with reinforcement learning (RL), aiming to improve the decision-making capabilities of autonomous systems. By exploiting quantum principles like superposition and entanglement, QRL offers notable improvements over classical RL in areas such as computational speed, accuracy, and energy efficiency. This paper presents detailed empirical results, simulations, and theoretical models demonstrating the superiority of QRL over classical methods, particularly in high-complexity environments like autonomous drones and self-driving cars. We also explore the trade-offs, including computational cost, hardware limitations, and energy consumption, providing a balanced view of QRL's potential and limitations for real-world applications.