Deep Learning and Optimization Approaches in Reflection Equivariant Quantum Neural Networks Based Human Resources Recruitment System for Business Process Management: A Review

Abstract

The rapid advancement of artificial intelligence and quantum computing has significantly transformed human resource management, particularly in recruitment and talent acquisition. Traditional machine learning approaches often struggle with high-dimensional, imbalanced, and bias-prone recruitment data, limiting their scalability and fairness. These challenges have driven the development of advanced frameworks capable of improving decision-making efficiency and accuracy in modern business process management systems. This paper presents a comprehensive review of Reflection Equivariant Quantum Neural Networks (REQNNs) for intelligent recruitment systems. REQNNs integrate symmetry constraints into quantum neural architectures, enabling 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 among candidate attributes while reducing overfitting and improving robustness. Applications include resume screening, candidate ranking, and workforce planning within hybrid classical-quantum systems. The review highlights optimization techniques such as quantum natural gradients and evolutionary strategies to address challenges like data imbalance and training instability. Empirical findings indicate improved predictive performance, fairness, and scalability compared to classical methods. Despite these advancements, limitations related to quantum hardware and system integration persist, emphasizing the need for further research in scalable and interpretable recruitment systems.