Computational Methods in Quantum Social Science: Innovative Theoretical, Interdisciplinary, and Empirical Approaches

Abstract

This paper proposes an innovative approach to social science research based on quantum theory, integrating quantum probability, quantum game theory, and quantum statistical methods into a comprehensive interdisciplinary framework for both theoretical and empirical investigation. The study elaborates on how core quantum concepts such as superposition, interference, and measurement collapse can be applied to model social decision making, cognition, and interactions. Advanced quantum computational methods and algorithms are employed to transition from theoretical model development to simulation and experimental validation. Through case studies in international relations, economic games, and political decision making, the research demonstrates that quantum models possess significant advantages in explaining irrational and context-dependent behaviors that traditional methods often fail to capture. The paper also explores the potential applications of quantum social science in policy formulation and public decision making, addresses the ethical, privacy, and social equity challenges posed by quantum artificial intelligence, and outlines future research directions at the convergence of quantum AI, quantum machine learning, and big data analytics. The findings suggest that quantum social science not only offers a novel perspective for understanding complex social phenomena but also lays the foundation for more accurate and efficient systems in social forecasting and decision support.