On the Stability of Local Interpretable Model-Agnostic Explanations for Quantum Classifiers

Abstract The interplay between machine learning and quantum computing can lead to unprecedented prospects for both fundamental research and real-world applications. In this new field of quantum machine learning, quantum classifiers, i.e., quantum algorithms for solving classification problems, have attracted the most attention. However, despite the promising performance of these new models for classification tasks, they are complex black-box models. Therefore, like or even more so than classical machine learning models, their use in critical contexts such as medical diagnosis could be hampered without the support of explanations. This paper addresses this challenge by investigating the use of Local Interpretable Model-agnostic Explanations (LIME) for quantum classifiers, with a specific focus on the stability of the explanations. Stability is a key property for ensuring that explanations are consistent and trustworthy, especially when decisions impact sensitive domains. The paper conducts a systematic stability study of LIME in the quantum setting, validating whether a classical explainable AI technique can be meaningfully applied to quantum models. As shown in experiments involving different datasets belonging to critical applications, LIME provides explanations for quantum support vector machines and quantum neural networks that are not only interpretable but also stable. By highlighting the stability of explanations, these findings demonstrate the suitability of LIME as an explainability tool for quantum classifiers.