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<p>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.</p>

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ExQUAL: an explainable quantum machine learning classifier

  • 期刊论文

  • 发表日期：

    2025年8月28日

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  • Karuna Kadian,
  • Sunita Garhwal,
  • Ajay Kumar

摘要

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.