Q-XAI: Towards Explainable Quantum Neural Networks for Human-Centered Intelligence

Artificial intelligence (AI) is transforming society, from healthcare diagnostics to financial decision-making, yet its rapid growth has outpaced our ability to understand its reasoning. Complex models, particularly deep neural networks, operate largely as “black boxes," raising critical concerns about trust, accountability, and fairness. Explainable AI (XAI) has emerged to illuminate these opaque systems, enabling human-understandable explanations and fostering reliable deployment. At the same time, quantum computing introduces Quantum Neural Networks (QNNs), which exploit superposition, entanglement, and non-local correlations to process information in fundamentally new ways, promising breakthroughs in speed, scalability, and modeling complexity. However, these quantum advantages come with unique interpretability challenges: probabilistic outputs, entangled states, and complex causal dependencies make traditional XAI approaches insufficient. In this position paper, we propose Q-XAI, a comprehensive framework that integrates explainability into quantum machine learning. We discuss its core principles, architectural components, and applications across healthcare, finance, and neuroscience, highlighting its potential to bridge human understanding with quantum intelligence. We further outline challenges, ethical considerations, and future research directions. By providing a roadmap for interpretable quantum AI, this work addresses the urgent need for trustworthy, transparent, and human-centered quantum intelligence.