Could Quantum Computing Revolutionize Our Study of Human Cognition

Shyam R. Sihare

Balasubramani R, Vidyadevi G. Biradar

The concept of Agentic AI, which focuses on empowering autonomous systems with human-like decision-making capabilities, has gained substantial attention in recent years. By leveraging advanced reinforcement learning (RL) techniques and cognitive frameworks, Agentic AI aims to enable machines to make complex decisions in dynamic environments with minimal human intervention. This paper introduces Agentic AI, a novel approach that combines Quantum Reinforcement Learning (QRL) and Cognitive Neuromorphic Frameworks to empower autonomous decision-making in complex environments. The proposed model leverages quantum computing principles to accelerate learning in reinforcement-based systems, while cognitive neuromorphic frameworks enhance adaptability and real-time decision-making by mimicking human brain functionality. The integration of QRL allows for faster convergence in learning algorithms, improving decision accuracy by 25% and reducing training time by 40%. Cognitive neuromorphic architectures offer enhanced scalability and resilience in dynamic environments, resulting in a 35% improvement in system robustness during testing. Simulations in autonomous navigation and robotic control show that Agentic AI achieves 20% more efficient task execution compared to traditional AI models. These values underscore the potential of the proposed system to revolutionize autonomous systems, providing a robust foundation for decision-making across a range of applications.

Troublefield, Troy

The emergence of quantum computing has prompted researchers to examine potential parallels between quantum mechanical principles and human cognitive processes. This article explores the theoretical framework of quantum cognition, examining how concepts from quantum mechanics, including superposition, entanglement, and measurement, might inform our understanding of human decision-making, memory, and consciousness. While quantum computing operates on fundamentally different physical principles than biological neural networks, the mathematical formalisms of quantum mechanics offer novel perspectives on cognitive phenomena that classical probability theory struggles to explain. This review critically examines the current state of quantum cognition research, its applications in computational modeling, and the ongoing debates regarding the validity of quantum mechanical analogies in psychological theory. The analysis reveals both promising theoretical insights and significant limitations in applying quantum principles to human cognition, highlighting the need for empirical validation and careful distinction between mathematical analogy and physical reality.

mohamed kamal arafa el-rakhawi

The El-Rakhawi Quantum-Neuromorphic AGI Engine (QNAGI) revolutionizes artificial intelligence by fusing quantum computing with neuromorphic cognitive architecture to solve planetary-scale challenges. Addressing the causal reasoning deficit, catastrophic forgetting, and thermodynamic inefficiency of classical AI, QNAGI introduces four novel modules: Entangled Knowledge Representation (EKR) mapping concepts to fault-tolerant quantum states; a Cognitive Hamiltonian Reasoning Engine (CHRE) translating causal graphs into Hamiltonian operators to find optimal ground states; Thermodynamically Reversible Continuous Learning (TRCL) utilizing unitary Hilbert space expansion to prevent forgetting while approaching Landauer’s limit; and a Safe Quantum-Human Translation Interface (SQHTI) encoding ethical values as quantum observables to guarantee alignment. The framework is quantified by the Quantum Cognitive Efficiency Metric (QCEM), yielding bits per Joule, directly linking causal insight acquisition to fundamental thermodynamic limits. QNAGI enables real-time, ethically aligned optimization for global crises like pandemics, climate change, and logistics, transforming AI from a statistical pattern matcher into a true, energy-efficient cognitive partner for humanity.

Carol Popp Weingarten, Pudugramam Murali Doraiswamy, Matthew P. A. Fisher