QuXAI: Explainers for Hybrid Quantum Machine Learning Models

2025-05-15

AI Summary

Hybrid quantum-classical machine learning (HQML) models—combining quantum feature encoding with classical learning architectures—suffer from a critical lack of interpretability. Method: This paper introduces Q-MEDLEY, the first unified eXplainable AI (XAI) framework specifically designed for this paradigm. It enables quantum-classical joint attribution without compromising the integrity of quantum feature mappings, via three core mechanisms: perturbation-driven feature importance estimation, differentiable attribution visualization, and noise-decoupling for disentangled explanations—supporting both global interpretability and local instance-level attribution. Contribution/Results: Empirical evaluation shows Q-MEDLEY matches state-of-the-art classical XAI methods on standard benchmarks; ablation studies confirm synergistic module effectiveness. The framework significantly enhances HQML model transparency, trustworthiness, and decision traceability, providing essential interpretability infrastructure for safe, controllable quantum-enhanced AI.

Technology Category

• Machine Learning: Quantum Machine Learning
• Humans and AI: Explainable AI (XAI) for Human Understanding
• Philosophy and Ethics of AI: Accountability, Interpretability & Explainability

Application Category

• User Modeling, Personalization and Recommendation: Explainable and interpretable methods for personalization
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

The emergence of hybrid quantum-classical machine learning (HQML) models opens new horizons of computational intelligence but their fundamental complexity frequently leads to black box behavior that undermines transparency and reliability in their application. Although XAI for quantum systems still in its infancy, a major research gap is evident in robust global and local explainability approaches that are designed for HQML architectures that employ quantized feature encoding followed by classical learning. The gap is the focus of this work, which introduces QuXAI, an framework based upon Q-MEDLEY, an explainer for explaining feature importance in these hybrid systems. Our model entails the creation of HQML models incorporating quantum feature maps, the use of Q-MEDLEY, which combines feature based inferences, preserving the quantum transformation stage and visualizing the resulting attributions. Our result shows that Q-MEDLEY delineates influential classical aspects in HQML models, as well as separates their noise, and competes well against established XAI techniques in classical validation settings. Ablation studies more significantly expose the virtues of the composite structure used in Q-MEDLEY. The implications of this work are critically important, as it provides a route to improve the interpretability and reliability of HQML models, thus promoting greater confidence and being able to engage in safer and more responsible use of quantum-enhanced AI technology.

Problem

• Addressing black box behavior in hybrid quantum-classical machine learning models
• Developing robust explainability approaches for HQML with quantized feature encoding
• Improving interpretability and reliability of quantum-enhanced AI technology

Innovation

• HQML models with quantum feature maps
• Q-MEDLEY for feature importance explanation
• Combines quantum transforms with classical XAI