Year: 2025
Type: article
Abstract: : This research explores the emerging paradigm of Quantum Intelligence (QI), which unifies artificial intelligence and quantum computing to enable next-generation cognitive systems. By leveraging quantum principles such as superposition, entanglement, and quantum tunneling, QI frameworks transcend classical AI limitations in learning efficiency, contextual awareness, and problem-solving complexity. The study implements a hybrid architecture integrating variational quantum circuits with reinforcement learning agents, tested across multi-goal environments. Experimental results reveal faster convergence, greater entropy reduction, and enhanced generalization compared to classical deep Q-networks. These findings demonstrate the transformative potential of QI to replicate and surpass human-like cognition, offering scalable, efficient, and adaptable models for future autonomous reasoning systems. Keywords: Quantum intelligence, Artificial intelligence, Quantum computing, Hybrid AI systems, Reinforcement learning, Variational quantum circuits, Cognitive systems, Quantum-enhanced learning, Entropy optimization, Next-generation AI
Author: Murali Krishna Pasupuleti
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: bronze
OpenAlex ID: W4410413414