<ul><li>Hybrid quantum-classical machine learning models bring new possibilities in computational intelligence but their complexity often results in black-box behavior.</li><li>A research gap exists in explainability approaches for HQML architectures that combine quantum feature encoding and classical learning, leading to the development of QuXAI framework.</li><li>QuXAI, based on Q-MEDLEY explainer, helps in understanding feature importance in hybrid systems by showcasing classical aspects and reducing noise.</li><li>This work aims to enhance interpretability and reliability of HQML models, ensuring safer and more responsible use of quantum-enhanced AI technology.</li></ul>

QuXAI: Explainers for Hybrid Quantum Machine Learning Models

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