A Hybrid Approach Integrating Fuzzy Logic, Neural-Fuzzy Systems, and Quantum Machine Learning for Enhanced Decision-Making in Complex Systems

Abstract

The increasing complexity of modern decision-making systems necessitates the integration of advanced artificial intelligence (AI) techniques. Hybrid AI models that incorporate fuzzy logic, neural-fuzzy systems, and quantum machine learning (QML) offer a promising solution for handling uncertainty, improving interpretability, and accelerating computations. This paper explores how these paradigms complement each other to enhance AI-based decision-making. A systematic review of pre-2023 literature highlights advancements in hybrid AI, showcasing applications in medical diagnosis, finance, and cybersecurity. The study presents an experimental analysis comparing hybrid models against traditional machine learning techniques. Finally, we discuss emerging trends and challenges, emphasizing the need for robust, scalable hybrid AI frameworks.