Artificial Intelligence Techniques for Reflection Equivariant Quantum Neural Networks Based Human Resources Recruitment System for Business Process Management: Trends and Challenges

Abstract

The convergence of artificial intelligence, quantum computing, and organizational management is transforming human resource recruitment systems. Traditional machine learning approaches, while effective, face limitations in scalability, bias mitigation, and handling complex, high-dimensional recruitment data. These challenges have driven the need for advanced computational models capable of improving efficiency, fairness, and decision-making in modern business process management. This paper presents a comprehensive review of Reflection Equivariant Quantum Neural Networks (REQNNs) for recruitment automation. By incorporating symmetry constraints into quantum neural architectures, REQNNs ensure consistent and generalized learning across structured candidate data. Leveraging quantum principles such as superposition and entanglement, these models enhance feature representation and capture complex relationships while reducing overfitting and improving robustness. Applications include resume screening, candidate ranking, and workforce planning within hybrid quantum-classical frameworks. The review highlights optimization techniques such as variational quantum circuits, Bayesian tuning, and quantum generative models to address challenges like data imbalance and scalability. Empirical findings indicate improved predictive accuracy and fairness compared to classical approaches. However, limitations in quantum hardware, interpretability, and enterprise integration remain, emphasizing the need for further research in scalable and practical quantum-enhanced recruitment systems.