Image Classification and Explainability Analysis Using GPU-based CUDA-Q Hybrid Quantum Neural Networks

Abstract

Explainable artificial intelligence (XAI) remains underexplored in quantum machine learning (QML) due to limited quantum computational resources and the lack of practical interpretability techniques. In this study, we implement the Local Interpretable Model-Agnostic Explanations (LIME) algorithm within the CUDA-Q framework and propose an interpretable QML analysis method using GPU-accelerated quantum simulation. All experiments are conducted on a GPU-based simulator rather than real quantum hardware, enabling stable large-scale sampling required by LIME and ensuring reproducible results. We design a binary classification model based on a single-qubit variational circuit composed of parameterized RY and RX rotations, and the model is trained with gradient-based optimization. The performance is evaluated on the MNIST, KMNIST, Fashion-MNIST, and CIFAR-10 datasets using training and test accuracy as metrics. By applying LIME, the classification outcomes of the quantum model are visualized and interpreted in a human-understandable form, demonstrating that meaningful interpretability can be achieved even under limited qubit counts and constrained computational environments. Furthermore, we analyze the trade-off between simulator performance and model accuracy, confirming that GPU-based CUDA-Q simulation provides a scalable and practical research environment for QML. This work offers a concrete pathway toward transparent and interpretable quantum machine learning