Many Machine Learning (ML) models are referred to as black box models, providing no real insights into why a prediction is made. Feature importance and explainability are important for increasing transparency and trust in ML models, particularly in settings such as healthcare and finance. With quantum computing's unique capabilities, such as leveraging quantum mechanical phenomena like superposition, which can be combined with ML techniques to create the field of Quantum Machine Learning (QML), and such techniques may be applied to QML models. This article explores feature importance and explainability insights in QML compared to Classical ML models. Utilizing the widely recognized Iris dataset, classical ML algorithms such as SVM and Random Forests, are compared against hybrid quantum counterparts, implemented via IBM's Qiskit platform: the Variational Quantum Classifier (VQC) and Quantum Support Vector Classifier (QSVC). This article aims to provide a comparison of the insights generated in ML by employing permutation and leave one out feature importance methods, alongside ALE (Accumulated Local Effects) and SHAP (SHapley Additive exPlanations) explainers.
Many Machine Learning (ML) models are referred to as black box models, providing no real insights into why a prediction is made. Feature importance and explainability are important for increasing transparency and trust in ML models, particularly in settings such as healthcare and finance. With quantum computing's unique capabilities, such as leveraging quantum mechanical phenomena like superposition, which can be combined with ML techniques to create the field of Quantum Machine Learning (QML), and such techniques may be applied to QML models. This article explores feature importance and explainability insights in QML compared to Classical ML models. Utilizing the widely recognized Iris dataset, classical ML algorithms such as SVM and Random Forests, are compared against hybrid quantum counterparts, implemented via IBM's Qiskit platform: the Variational Quantum Classifier (VQC) and Quantum Support Vector Classifier (QSVC). This article aims to provide a comparison of the insights generated in ML by employing permutation and leave one out feature importance methods, alongside ALE (Accumulated Local Effects) and SHAP (SHapley Additive exPlanations) explainers.
Explainable ML algorithms are designed to provide transparency and insight into their decision-making process. Explaining how ML models come to their prediction is critical in fields such as healthcare and finance, as it provides insight into how models can help detect bias in predictions and help comply with GDPR compliance in these fields. QML leverages quantum phenomena such as entanglement and superposition, offering the potential for computational speedup and greater insights compared to classical ML. However, QML models also inherit the black-box nature of their classical counterparts, requiring the development of explainability techniques to be applied to these QML models to help understand why and how a particular output was generated. This paper will explore the idea of creating a modular, explainable QML framework that splits QML algorithms into their core components, such as feature maps, variational circuits (ansatz), optimizers, kernels, and quantum-classical loops. Each component will be analyzed using explainability techniques, such as ALE and SHAP, which have been adapted to analyse the different components of these QML algorithms. By combining insights from these parts, the paper aims to infer explainability to the overall QML model.
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.
Quantum machine learning (QML) has emerged as a promising paradigm for solving complex classification problems by leveraging the computational advantages of quantum systems. While most traditional machine learning models focus on clean, balanced datasets, real-world data is often noisy, imbalanced and high-dimensional, posing significant challenges for scalability and generalisation. This paper conducts an extensive experimental evaluation of five supervised classifiers- Decision Tree, K nearest neighbour, Random Forest, linear regression and support vector machines in comparison with Quantum machine learning classifiers- quantum Support vector machine, quantum k- nearest neighbor and variational quantum classifier-across five diverse datasets, including iris, wine quality, Breast cancer, UCI human activity recognition, and Pima diabetes. To simulate real-world challenges, we introduce class imbalance using SMOTE and ADASYN Sampling, inject Gaussian noise into the features, and assess the impact of dimensionality reduction through ANOVA-based feature selection. Additionally, we utilise explainable AI tools, such as SHAP and LIME, to interpret model decisions. Our results demonstrate that Logistic Regression consistently performs well under various complexities, while Quantum Support Vector Machines show resilience to feature noise and class imbalance. The study also highlights the current capabilities and limitations of QML models, offering valuable insights into building generalisable and interpretable ML systems for deployment in complex environments. These insights are crucial for building robust, interpretable, and generalisable ML models for practical deployment.
Predictor importance is a crucial part of data preprocessing pipelines in classical and quantum machine learning (QML). This work presents the first study of its kind in which feature importance for QML models has been explored and contrasted against their classical machine learning (CML) equivalents. We developed a hybrid quantum-classical architecture where QML models are trained and feature importance values are calculated from classical algorithms on a real-world dataset. This architecture has been implemented on ESPN Fantasy Football data using Qiskit statevector simulators and IBM quantum hardware such as the IBMQ Mumbai and IBMQ Montreal systems. Even though we are in the Noisy Intermediate-Scale Quantum (NISQ) era, the physical quantum computing results are promising. To facilitate current quantum scale, we created a data tiering, model aggregation, and novel validation methods. Notably, the feature importance magnitudes from the quantum models had a much higher variation when contrasted to classical models. We can show that equivalent QML and CML models are complementary through diversity measurements. The diversity between QML and CML demonstrates that both approaches can contribute to a solution in different ways. Within this paper we focus on Quantum Support Vector Classifiers (QSVC), Variational Quantum Circuit (VQC), and their classical counterparts. The ESPN and IBM fantasy football Trade Assistant combines advanced statistical analysis with the natural language processing of Watson Discovery to serve up personalized trade recommendations that are fair. Here, player valuation data of each player has been considered and this work can be extended to calculate the feature importance of other QML models such as Quantum Boltzmann machines.
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.
The emerging field of Quantum Machine learning (QML), which offers noticeably faster processing speed, is the result of the merger of Quantum Computing (QC) and Machine Learning (ML). Superposition and Entanglement are the fundamental concepts of QC, which increases computational scalability. This comprehensive overview examines QML foundations, algorithms, applications, and challenges of QML. When applied to the Cleveland Heart Disease Dataset, the Quantum Support Vector Classifier (QSVC) achieves an accuracy of 0.75 whereas the Variational Quantum Classifier (VQC) yields an accuracy of 0.62, thus showing that QSVC outperforms VQC. The main issue in QML that calls for effort and investment is noise. QML has the potential to advance technology and has its application in a variety of domains.