Explainable Quantum Neural Networks: Example-Based and Feature-Based Methods

Quantum neural networks (QNNs) are gaining attention for their potential, but their lack of interpretability remains a barrier to wider adoption. In this paper, we adapt and extend explainability techniques commonly used in classical neural networks to the quantum domain, making QNNs more transparent and interpretable. By applying both feature-based and example-based methods, we provide a comprehensive analysis of how QNNs generate predictions. Our results demonstrate that these adapted techniques offer valuable insights into the internal mechanisms of QNNs, paving the way for more reliable and trustworthy quantum machine learning models. This work contributes to improving the explainability of QNNs, enhancing their applicability in complex, real-world scenarios.