A Comprehensive Review of Reflection Equivariant Quantum Neural Networks Based Human Resources Recruitment System for Business Process Management

Abstract

The rapid advancement of artificial intelligence and quantum computing is transforming human resource management, particularly in recruitment and talent acquisition. Traditional business process management systems rely on classical methods that struggle to handle the complexity, scale, and multidimensional nature of modern recruitment data. These limitations have driven the exploration of quantum-based approaches capable of improving efficiency, scalability, and decision quality. This paper reviews the application of Reflection Equivariant Quantum Neural Networks (REQNNs) in recruitment systems. REQNNs incorporate symmetry constraints within quantum circuits, ensuring consistent outputs under reflection transformations and improving generalization. Built on parameterized quantum circuits, these models leverage quantum principles such as superposition and entanglement to capture complex patterns in candidate data, including skills, competencies, and behavioral attributes. The integration of equivariance reduces redundancy and enhances robustness in decision-making processes. Applications of REQNNs include automated resume screening, candidate evaluation, and workforce planning within hybrid classical-quantum systems. Empirical studies using datasets such as IBM HR Analytics and LinkedIn Talent Insights demonstrate improved fairness and predictive performance. Despite their potential, challenges remain in quantum hardware limitations, system integration, and scalability. This review highlights the transformative potential of quantum-enhanced recruitment systems and outlines future directions for intelligent, fair, and efficient HR management solutions.