This research paper explores the explainability of decision-making in hybrid quantum–classical classification models. Quantum Machine Learning (QML) combines the power of quantum computing with classical machine learning techniques to solve complex computational problems. However, understanding how these hybrid models make decisions remains a significant challenge. The study focuses on analyzing hybrid quantum–classical architectures and evaluating methods that improve the interpretability of their predictions. Techniques from Explainable Artificial Intelligence (XAI) are applied to investigate the internal decision-making processes of the models. The research also compares classical machine learning approaches with quantum-enhanced models to understand their performance and transparency. The results demonstrate that explainability techniques can help researchers better interpret model predictions, identify important features, and improve trust in hybrid quantum–classical systems. This work contributes to the development of transparent and reliable quantum machine learning applications in areas such as healthcare, finance, and data analytics.
This research paper explores the explainability of decision-making in hybrid quantum–classical classification models. Quantum Machine Learning (QML) combines the power of quantum computing with classical machine learning techniques to solve complex computational problems. However, understanding how these hybrid models make decisions remains a significant challenge. The study focuses on analyzing hybrid quantum–classical architectures and evaluating methods that improve the interpretability of their predictions. Techniques from Explainable Artificial Intelligence (XAI) are applied to investigate the internal decision-making processes of the models. The research also compares classical machine learning approaches with quantum-enhanced models to understand their performance and transparency. The results demonstrate that explainability techniques can help researchers better interpret model predictions, identify important features, and improve trust in hybrid quantum–classical systems. This work contributes to the development of transparent and reliable quantum machine learning applications in areas such as healthcare, finance, and data analytics.
The rapid advancement of Artificial Intelligence (AI) has heightened concerns about the opacity of decision-making processes in complex models, particularly deep neural networks. This research explores a novel paradigm that integrates quantum computing principles into Explainable AI (XAI) to enhance interpretability without compromising predictive accuracy. The study introduces a Quantum-inspired Interpretable Model (QIIM) framework that leverages Hilbert space embeddings, unitary transformations, and operator-based learning to represent knowledge in a mathematically transparent form. The proposed model computes interpretability through the expectation values of observable operators, enabling the decomposition of decisions into quantifiable, human-understandable components. To capture hierarchical relationships, a Tensor Network Interpretable Model (TNIM) is further developed, offering scalable insights into complex dependencies among features. Experimental evaluations—performed on benchmark datasets for decision-making tasks—demonstrate that the quantum-inspired models achieve competitive accuracy while significantly improving local and global interpretability metrics compared to classical XAI techniques. The findings underscore the potential of quantum formalism as a new foundation for transparent AI systems, bridging the gap between computational efficiency and explainability. This study contributes to both theoretical understanding and practical advancement in interpretable machine learning, paving the way for ethically aligned, transparent, and human-trustworthy AI-driven decision support systems.
Artificial Intelligence (AI) systems have shown good success at classifying. However, the lack of explainability is a true and significant challenge, especially in high-stakes domains, such as health and finance, where understanding is paramount. We propose a new solution to this challenge: an explainable AI framework based on our comparative study with Quantum Boltzmann Machines (QBMs) and Classical Boltzmann Machines (CBMs). We leverage principles of quantum computing within classical machine learning to provide substantive transparency around decision-making. The design involves training both models on a binarised and dimensionally reduced MNIST dataset, where Principal Component Analysis (PCA) is applied for preprocessing. For interpretability, we employ gradient-based saliency maps in QBMs and SHAP (SHapley Additive exPlanations) in CBMs to evaluate feature attributions.QBMs deploy hybrid quantum-classical circuits with strongly entangling layers, allowing for richer latent representations, whereas CBMs serve as a classical baseline that utilises contrastive divergence. Along the way, we found that QBMs outperformed CBMs on classification accuracy (83.5% vs. 54%) and had more concentrated distributions in feature attributions as quantified by entropy (1.27 vs. 1.39). In other words, QBMs not only produced better predictive performance than CBMs, but they also provided clearer identification of "active ingredient" or the most important features behind model predictions. To conclude, our results illustrate that quantum-classical hybrid models can display improvements in both accuracy and interpretability, which leads us toward more trustworthy and explainable AI systems.
This work investigates a hybrid quantum-classical machine learning methodology that combines deep learning with quantum computing to improve predictive analytics. The method starts by changing basic input features into the best mixed forms using classical weight matrices and quantum state encoding. A unitary operator refines quantum states to enhance feature extraction and representation. Gradient-based alterations repeatedly modify feature weights during optimization to improve accuracy. Quantum circuits not only change data but also enhance system separability and resilience. Normalizing and enhancing extracted properties using quantum and classical transformations ensures stability and consistency. Performance trials demonstrate that the hybrid technique improves accuracy, precision, recall, computational complexity, execution time, and energy economy. The hybrid model always outperforms machine learning, deep learning, and quantum mechanics-based approaches. The technique is now more scalable, making enormous data sets easier to analyze. Faster convergence rates and noise tolerance improve model performance in real-world applications. The findings suggest that quantum advances and classical learning approaches improve model interpretability and decision-making reliability. The research found hybrid quantum-classical machine learning to be a powerful, scalable, and efficient predictive modeling solution. This method improves precision while conserving computing power. By using both paradigms, the technique provides deeper insights and better predictions. As a major facilitator for future AI-driven applications, the hybrid model provides a possible solution for difficult machine learning challenges. These developments make hybrid models available.
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.
Abstract The emergence of hybrid quantum-classical machine learning (HQML) models opens new horizons of computational intelligence but their fundamental complexity frequently leads to black box behavior that undermines transparency and reliability in their application. Although XAI for quantum systems still in its infancy, a major research gap is evident in robust global and local explainability approaches that are designed for HQML architectures that employ quantized feature encoding followed by classical learning. The gap is the focus of this work, which introduces QuXAI, an framework based upon Q-MEDLEY, an explainer for explaining feature importance in these hybrid systems. Our model entails the creation of HQML models incorporating quantum feature maps, the use of Q-MEDLEY, which combines feature based inferences, preserving the quantum transformation stage and visualizing the resulting attributions. Our result shows that Q-MEDLEY delineates influential classical aspects in HQML models, as well as separates their noise, and competes well against established XAI techniques in classical validation settings. Ablation studies more significantly expose the virtues of the composite structure used in Q-MEDLEY. The implications of this work are critically important, as it provides a route to improve the interpretability and reliability of HQML models, thus promoting greater confidence and being able to engage in safer and more responsible use of quantum-enhanced AI technology. Our code and experiments are open-sourced at: https://github.com/GitsSaikat/QuXAI
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.