A Survey of Methods and Architectures for Reflection Equivariant Quantum Neural Networks Based Human Resources Recruitment System for Business Process Management

Abstract

The rapid advancement of artificial intelligence and quantum computing has introduced new paradigms for intelligent systems capable of handling complex, high-dimensional data. In human resource recruitment, traditional machine learning approaches often face limitations in scalability, efficiency, and the ability to model intricate relationships among candidate attributes. These challenges have driven the exploration of quantum neural networks as a transformative solution for next-generation recruitment systems.

This paper presents a comprehensive review of Reflection Equivariant Quantum Neural Networks (REQNNs) for recruitment within business process management frameworks. REQNNs incorporate symmetry-preserving constraints into quantum circuits, ensuring consistent and generalized predictions under reflection transformations. Leveraging quantum principles such as superposition and entanglement, these models enable efficient representation of candidate data and improved learning of complex feature interactions while mitigating overfitting.

Applications include resume screening, candidate ranking, and talent analytics within hybrid classical-quantum architectures. The review highlights optimization techniques such as variational quantum algorithms and quantum natural gradients to address training challenges. Empirical findings suggest improved performance, fairness, and scalability compared to classical approaches. However, limitations such as hardware constraints, integration complexity, and interpretability remain, emphasizing the need for continued research in practical and scalable quantum-enhanced recruitment systems.