Quantum‐Inspired Soft Computing for Intelligent Data Processing in Real‐Life Scenarios

This chapter discusses the combination of quantum-inspired methods from one side and well-known soft computing techniques, such as fuzzy logic, genetic algorithms, and neural networks, to address numerous issues across domains, including healthcare, finance, and smart city infrastructure. This helps us to understand how these novel methods advance our ability to manipulate data and offer a glimpse of the power quantum-style computing offers. This chapter presents a careful analysis of quantum alternatives and their applications in intelligent data processing. The goal is to theoretically decompose the benefits and drawbacks of applying these methodologies across various domains while considering caveats such as computational costs, noisy data points, or generalizability of algorithms. This debate also has an ethical dimension, i.e., data privacy, bias, and fairness in AI decision making, which is a very important aspect of this discussion. With guardrails being placed around the ethics of applying quantum-inspired soft computing, it lays out good morals and tells us what might go great when used. It also showcases a range of practical quantum-inspired algorithms through several case studies that demonstrate how they enhance business decision-making and predictive analytics. The purpose of this study is to act as a source of motivation for further research efforts and implementation in the real world using quantum-inspired soft computing, with emphasis on its interdisciplinary nature, which can be exploited to solve pressing data-centric issues today.