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IEEE International Conference on Cognitive Machine Intelligence (CogMI)

Quantum Regression for Cognitive Intelligence in Complex Environments

Ahmad Alomari, S. B. Rathna Kumar

Published Nov 12, 2025• 2 citations
Publisher
DOI
10.1109/cogmi67134.2025.00047

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Quantum Intelligent Systems: A Hybrid Quantum-Logic Framework Integrating Physical, Control, and Cognitive Layers Author: Dr. Siamak Noorolahi Affiliation: SBMU Abstract Traditional intelligent systems are built upon the classical von Neumann architecture, which relies on deterministic logic and linear hierarchical processing. This paradigm faces significant limitations when addressing high-dimensional, non-linear, and stochastic environments. This paper proposes a novel framework for Quantum Intelligent Systems (QIS) based on Hybrid Quantum Logic. We introduce a multi-layered architecture comprising the Physical Layer, the Control Layer, and the Cognitive Layer, interconnected through quantum phenomena such as superposition and entanglement. Unlike classical systems where layers are strictly sequential, our framework treats the system as a unified quantum field where information processing, actuation, and cognition occur through the controlled collapse of a system-wide wave function. This approach provides a theoretical basis for achieving unprecedented levels of autonomy and strategic adaptability in complex robotic and autonomous systems. Keywords: Quantum Intelligent Systems (QIS), Hybrid Quantum Logic, Cognitive Layer, Entanglement-based Control, Wave Function Collapse in Decision Making.

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The integration of quantum computing and artificial intelligence (AI) has opened new frontiers for enhancing cognitive adaptability and advancing artificial general intelligence (AGI). This study explores the conceptual synergy between quantum–AI integration as an independent variable, cognitive adaptability as a mediating variable, and AGI cognitive performance as the dependent variable. By examining how quantum principles such as superposition and entanglement enhance AI’s capacity for non-linear reasoning, this research outlines a transformative model that aligns with human-like cognition. The study employs simulation-based analyses comparing classical and quantum–AI models across diverse cognitive tasks, revealing that quantum–AI systems outperform traditional AI in adaptability, problem generalization, and decision precision. The results indicate that quantum-enhanced learning enables faster adaptation and improved generalization, suggesting significant implications for cognitive modeling, human–machine collaboration, and AGI development. The theoretical contribution lies in expanding AGI’s cognitive architecture through quantum principles, while the practical implications include the development of adaptive, self-learning AI systems for complex, dynamic environments. Future research should explore empirical testing of quantum–AI frameworks, cross-disciplinary modeling between quantum cognition and neuroscience, and ethical considerations in autonomous decision-making systems.
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
Quantum Intelligent Systems: A Hybrid Quantum-Logic Framework Integrating Physical, Control, and Cognitive Layers Author: Dr. Siamak Noorolahi Affiliation: SBMU Abstract Traditional intelligent systems are built upon the classical von Neumann architecture, which relies on deterministic logic and linear hierarchical processing. This paradigm faces significant limitations when addressing high-dimensional, non-linear, and stochastic environments. This paper proposes a novel framework for Quantum Intelligent Systems (QIS) based on Hybrid Quantum Logic. We introduce a multi-layered architecture comprising the Physical Layer, the Control Layer, and the Cognitive Layer, interconnected through quantum phenomena such as superposition and entanglement. Unlike classical systems where layers are strictly sequential, our framework treats the system as a unified quantum field where information processing, actuation, and cognition occur through the controlled collapse of a system-wide wave function. This approach provides a theoretical basis for achieving unprecedented levels of autonomy and strategic adaptability in complex robotic and autonomous systems. Keywords: Quantum Intelligent Systems (QIS), Hybrid Quantum Logic, Cognitive Layer, Entanglement-based Control, Wave Function Collapse in Decision Making.