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.
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.
Quantum Machine Learning (QML) has emerged as a promising paradigm that leverages quantum mechanical principles to accelerate machine learning tasks.Despite its potential, most QML models remain opaque "black boxes," hindering their deployment in high-stakes or regulated domains where trust and accountability are essential.This work introduces two novel interpretability methods tailored for QML: Quantum Shapley Values (QSV) and Quantum Local Interpretable Model-Agnostic Explanations (Q-LIME).QSV adapts cooperative game theory to quantify the contribution of individual qubits, observables, or circuit parameters, while Q-LIME generates perturbed quantum states and trains surrogate classical models to approximate local decision boundaries.Through experiments on variational quantum classifiers and hybrid quantum-classical neural networks, we demonstrate that QSV provides rigorous attribution but suffers from scalability constraints, whereas Q-LIME yields efficient, approximate explanations suitable for near-term devices.By transforming quantum models from opaque "black boxes" into interpretable "glass boxes," this research lays the foundation for trustworthy, transparent, and human centred quantum AI.
Abstract As machine learning grows increasingly complex due to big data and deep learning, model explainability has become essential to fostering user trust. Quantum machine learning (QML) has emerged as a promising field, leveraging quantum computing to enhance classical machine learning methods, particularly through quantum representation learning (QRL). QRL aims to provide more efficient and powerful machine learning capabilities on noisy intermediate-scale quantum (NISQ) devices. However, interpreting QRL models poses significant challenges due to the reliance on quantum gate-based parameterized circuits, which, while analogous to classical neural network layers, operate in the quantum domain. To address these challenges, we propose an explainable QRL framework combining a quantum autoencoder (QAE) with a variational quantum classifier (VQC) and incorporating theoretical and empirical explainability for image data. Our dual approach enhances model interpretability by integrating visual explanations via local interpretable model-agnostic explanations (LIME) and analytical insights using Shapley Additive Explanations (SHAP). These complementary methods provide a deeper understanding of the model’s decision-making process based on prediction outcomes. Experimental evaluations on simulators and superconducting quantum hardware validate the effectiveness of the proposed framework for classification tasks, underscoring the importance of explainable representation learning in advancing QML towards more transparent and reliable applications.
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.
Machine learning models offer powerful predictive capabilities but often lack transparency. Local Interpretable Model-agnostic Explanations (LIME) addresses this by perturbing features and measuring their impact on a model's output. In text-based tasks, LIME typically removes present words (bits set to 1) to identify high-impact tokens. We propose \textbf{Q-LIME π} (Quantum LIME π), a quantum-inspired extension of LIME that encodes a binary feature vector in a quantum state, leveraging superposition and interference to explore local neighborhoods more efficiently. Our method focuses on flipping bits from 1→0 to emulate LIME's ``removal'' strategy, and can be extended to 0→1 where adding features is relevant. Experiments on subsets of the IMDb dataset demonstrate that Q-LIME π often achieves near-identical top-feature rankings compared to classical LIME while exhibiting lower runtime in small- to moderate-dimensional feature spaces. This quantum-classical hybrid approach thus provides a new pathway for interpretable AI, suggesting that, with further improvements in quantum hardware and methods, quantum parallelism may facilitate more efficient local explanations for high-dimensional data.
Abstract Interpretability of artificial intelligence (AI) methods, particularly deep neural networks, is of great interest. This heightened focus stems from the widespread use of AI-backed systems. These systems, often relying on intricate neural architectures, can exhibit behavior that is challenging to explain and comprehend. The interpretability of such models is a crucial component of building trusted systems. Many methods exist to approach this problem, but they do not apply straightforwardly to the quantum setting. Here, we explore the interpretability of quantum neural networks using local model-agnostic interpretability measures commonly utilized for classical neural networks. Following this analysis, we generalize a classical technique called LIME, introducing Q-LIME, which produces explanations of quantum neural networks. A feature of our explanations is the delineation of the region in which data samples have been given a random label, likely subjects of inherently random quantum measurements. We view this as a step toward understanding how to build responsible and accountable quantum AI models.
It is estimated that millions of people from different parts of the world are suffering from breast cancer. As of 2022, it has one of the highest mortality rates. It symptomatized by the abnormal growth of breast tissue cells aggregating into malignant tumors. Because of this, the current study investigates quantum-enhanced machine learning models in hope of curing the disease in its earlier stages. In this case, breast cancer tumors are deemed benign or malignant using three different datasets—Wisconsin Diagnostic Breast Cancer (WDBC), Mammographic Mass (MM) and Breast Cancer Wisconsin (Original), all of which are publicly accessible on the UCI Machine Learning Repository. These datasets differ greatly in terms of size and the number of features that make it suitable for performance analysis of breast cancer classification. On the Breast Cancer Wisconsin (Original) and WDBC datasets, Quantum Random Forest (QRF) has gained the maximum accuracy of 97% and 96%, respectively, suggesting superiority in high-dimensional feature sets. Moreover, Quantum Support Vector Machine (QSVM) showed promising results with 81% accuracy on the MM dataset. The latter result was achieved with the use of Explainable AI (XAI) approach with Local Interpretable Model-agnostic Explanations (LIME), which ensured interpretability and transparency of the quantum machine learning models. For each explanation LIME provided, there were important decision-altering features, like tumor size, lymph node involvement, degeneracy of malignancy and irradiation status, which were the primary determinants of the model’s choice. Such interpretability enhances trust in AI systems for medical diagnostics, as well as validating the outputs. Integrating quantum machine learning models with XAI techniques offers robust and interpretable frameworks for breast cancer classification, resulting in improved accuracy.