Quantum ComputingDriven Social Data Processing Methods

Abstract

Quantum computing introduces a revolutionary computational paradigm for social data analysis. Traditional social data processing methods face significant challenges in handling high-dimensional, heterogeneous, and dynamic datasets. Quantum computing, leveraging quantum superposition, entanglement, and parallel computation, provides efficient solutions for large-scale optimization, pattern recognition, and decision analysis. This paper systematically explores the applications of quantum computing in social sciences, covering theoretical foundations, methodological frameworks, experimental validations, and case studies to highlight the advantages of quantum algorithms in social network analysis, sentiment classification, and economic forecasting. Additionally, we discuss the broader implications of quantum computing for social science research paradigms, ethical and governance concerns, and future development directions, emphasizing the integration of quantum computing with artificial intelligence. This paper aims to provide new insights into computational social science and advance the practical adoption of quantum computing in social data processing.