A Quantum-inspired Fuzzy Soft Computing and Machine Learning Framework for Adaptive and Intelligent Decision Making under Uncertainty
DOI:
https://doi.org/10.46610/JoFSFLD.2026.v03i01.005Keywords:
Evolutionary algorithms, Fuzzy logic optimization, Intelligent decision-making systems, Machine learning integration, Quantum-inspired computing, Soft computing systemsAbstract
The growing complexity and uncertainty of real-world decision-making systems require sophisticated computational systems that surpass the constraints of classical artificial intelligence. As a new quantum-inspired soft computing and machine learning architecture, this study proposes a novel quantum-inspired next-generation intelligent decision-making based on the principles of fuzzy logic, neural networks, evolutionary computation, and quantum-inspired optimization methods. The suggested framework leverages the concepts of probabilistic superposition and parallelism in quantum computing, but does not require quantum hardware to make learning more efficient, explore the solution space, and be resilient to uncertainty. The architecture is a combination of fuzzy inference systems to deal with imprecision, machine learning models to deal with adaptive pattern recognition and quantum-inspired algorithms to deal with global optimization and feature selection. The framework also includes interpretable AI processes so that the decisions made by the framework can be transparent and explainable. Using experimental analysis of various fields, such as healthcare, diagnostic and financial risk analysis and smart city management, accuracy, convergence, and reliability in decision making are found to be better than conventional soft computing and machine learning methods. The findings present possibilities of quantum-inspired hybrid intelligent systems in dealing with complex, uncertain and dynamic environments. The study presents a single paradigm of combining quantum-inspired computation with soft computing and machine learning, which will enable the realization of scalable, interpretable, and high-performance intelligent decision-making systems in future applications of AI.
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