ExQUAL: an explainable quantum machine learning classifier

ExQUAL: an explainable quantum machine learning classifier

연구 분야: Artificial Intelligence

학회: Applied Intelligence

Quantum machine learning (QML) holds the potential to solve complex tasks that classical machine learning is unable to handle. QML is a promising and emerging field which is in the state of continuous development. This necessitates a deeper comprehension of the intricate black-box nature of the quantum machine learning models. To address this challenge, the incorporation of explainable artificial intelligence becomes imperative. This paper introduces a novel approach - Explainable Quantum Classifier (ExQUAL) to integrate the Local Interpretable Model-agnostic Explanations (LIME) framework and SHapley Additive exPlanations (SHAP) with the Pegasos Quantum Support Vector Machine (QSVM) model for classification tasks. ExQUAL provides a methodology to integrate these frameworks with both binary and multi-class classification tasks and provides both local and global explanations. This approach seeks to enhance transparency and interpretability while advancing the applicability and trustworthiness of quantum machine learning methodologies.