Recent Advances in Reflection Equivariant Quantum Neural Networks Based Human Resources Recruitment System for Business Process Management: A Systematic Review

Abstract

The convergence of quantum computing, equivariant neural networks, and human resource management has introduced a transformative approach to recruitment and talent acquisition. Traditional machine learning methods often struggle with scalability, bias mitigation, and the ability to model complex relationships in high-dimensional recruitment data. These limitations highlight the need for advanced computational frameworks capable of improving efficiency, fairness, and decision-making in modern business process management systems. This paper presents a systematic review of Reflection Equivariant Quantum Neural Networks (REQNNs) and their application in recruitment systems. By incorporating reflection symmetry constraints into quantum circuit architectures, REQNNs ensure consistent and generalized outputs across structured candidate data. Leveraging quantum principles such as superposition and entanglement, these models enhance representation learning while reducing overfitting and improving robustness in evaluating skills, competencies, and behavioral patterns. Applications of REQNNs span resume screening, candidate ranking, and workforce planning within hybrid quantum-classical frameworks. Empirical studies using datasets such as IBM HR Analytics and LinkedIn-based corpora demonstrate improvements in predictive accuracy, fairness, and process efficiency. Despite these advancements, challenges remain in quantum hardware scalability, integration with enterprise systems, and practical deployment. This review highlights the potential of quantum-enhanced, symmetry-aware models for developing intelligent and fair recruitment systems in modern organizations.