Modeling Human Creativity and Problem-Solving with Quantum-Inspired Cognitive Architectures
DOI:
https://doi.org/10.62802/vgpdeb05Keywords:
quantum-inspired cognition, creativity modeling, problem-solving, cognitive architectures, divergent thinking, contextuality, artificial intelligence, human–machine cognitionAbstract
Creativity and problem-solving are central components of human intelligence, yet they remain difficult to model within classical cognitive and computational frameworks. Traditional approaches, grounded in symbolic reasoning or probabilistic optimization, often struggle to capture the non-linear, context-sensitive, and exploratory nature of creative thought. Quantum-inspired cognitive architectures offer an alternative modeling paradigm by drawing on mathematical principles from quantum probability and quantum information theory—such as superposition, interference, and contextuality—without assuming physical quantum processes in the brain. This paper examines how quantum-inspired models can represent creative cognition as a dynamic process involving parallel idea generation, contextual recombination, and non-deterministic insight formation. By conceptualizing mental states as superposed cognitive representations and problem-solving as a sequence of state transitions influenced by contextual measurements, quantum-inspired architectures provide a unified framework for modeling divergent thinking, insight, and adaptive reasoning. The study synthesizes theoretical foundations from quantum cognition, creativity research, and artificial intelligence, and discusses implications for the design of intelligent systems that more closely emulate human creative and problem-solving capabilities.
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