In recent years, critical infrastructure systems—from smart grids to industrial automation platforms to cyber-physical environments—have become increasingly susceptible to failures, cyber attacks, and equipment deterioration. Many Machine Learning (ML) and Deep Learning (DL) models achieve high predictive accuracy but are difficult to interpret and require significant computational resources to deploy in safety-critical applications. The study introduced a new Explainable Quantum Artificial Intelligence (XQAI) framework for predicting failures in critical infrastructures, combining Quantum Adaptive Feature Embedding (QAFE), Variational Quantum Circuits (VQC), Bidirectional LSTM temporal learning, Quantum Attention mechanisms, and SHAP-based explainability analysis. The proposed framework leveraged heterogeneous data collected from Industrial Internet of Things (IIoT) sensors and SCADA systems to detect complex nonlinear patterns in infrastructure failures. The proposed XQAI framework was evaluated experimentally and demonstrated a very high accuracy of 98.9%, precision of 98.5%, recall of 98.1%, and F1-score of 98.3%, which are significantly higher than those of conventional machine learning, deep learning, and quantum learning models. The explainability module accurately identified the parameters that influenced infrastructure failure, particularly temperature, vibration, voltage instability, and communication latency. The results showed that integrating Explainable Artificial Intelligence (XAI) with quantum-augmented temporal learning had a remarkable impact on prediction reliability, interpretability, and computational efficiency. The proposed framework offers an intelligent infrastructure-monitoring solution that is scalable and transparent for the next-generation safety-critical industrial environment.
In recent years, critical infrastructure systems—from smart grids to industrial automation platforms to cyber-physical environments—have become increasingly susceptible to failures, cyber attacks, and equipment deterioration. Many Machine Learning (ML) and Deep Learning (DL) models achieve high predictive accuracy but are difficult to interpret and require significant computational resources to deploy in safety-critical applications. The study introduced a new Explainable Quantum Artificial Intelligence (XQAI) framework for predicting failures in critical infrastructures, combining Quantum Adaptive Feature Embedding (QAFE), Variational Quantum Circuits (VQC), Bidirectional LSTM temporal learning, Quantum Attention mechanisms, and SHAP-based explainability analysis. The proposed framework leveraged heterogeneous data collected from Industrial Internet of Things (IIoT) sensors and SCADA systems to detect complex nonlinear patterns in infrastructure failures. The proposed XQAI framework was evaluated experimentally and demonstrated a very high accuracy of 98.9%, precision of 98.5%, recall of 98.1%, and F1-score of 98.3%, which are significantly higher than those of conventional machine learning, deep learning, and quantum learning models. The explainability module accurately identified the parameters that influenced infrastructure failure, particularly temperature, vibration, voltage instability, and communication latency. The results showed that integrating Explainable Artificial Intelligence (XAI) with quantum-augmented temporal learning had a remarkable impact on prediction reliability, interpretability, and computational efficiency. The proposed framework offers an intelligent infrastructure-monitoring solution that is scalable and transparent for the next-generation safety-critical industrial environment.
The rapid digitalization of manufacturing systems has led to the widespread adoption of Industrial Internet of Things (IIoT) networks, enabling real-time monitoring, data-driven optimization, and intelligent automation. A critical application within this paradigm is predictive maintenance, which aims to anticipate equipment failures before they occur, thereby reducing downtime, operational costs, and safety risks. However, traditional machine learning approaches for predictive maintenance and anomaly detection often struggle with high-dimensional sensor data, complex nonlinear relationships, and scalability constraints in large industrial environments. This paper explores the emerging role of Quantum Machine Learning (QML) as a novel computational framework for enhancing predictive maintenance and anomaly detection in smart manufacturing systems. By leveraging quantum principles such as superposition and quantum-enhanced feature spaces, QML algorithms offer new possibilities for processing complex industrial data more efficiently and accurately. The study examines hybrid quantum–classical models for fault prediction, early anomaly detection, and pattern recognition in IIoT networks, highlighting their potential advantages over classical methods. Challenges related to hardware limitations, data encoding, and industrial deployment are also discussed, providing a balanced perspective on the feasibility and future impact of quantum-enhanced intelligence in smart manufacturing.
The integration of Quantum Computing with Artificial Intelligence (AI) presents a transformative opportunity for advanced medical imaging analysis. This paper proposes an Explainable Quantum AI (XQAI) framework for radiology decision support systems, aimed at enhancing diagnostic accuracy while ensuring interpretability of results. The approach leverages quantum kernel-based feature extraction to capture complex spatial–spectral patterns in MRI, CT, and PET scans, enabling superior detection of subtle anomalies. A hybrid quantum- classical deep learning model is employed, where quantum circuits perform high-dimensional feature mapping, and classical neural networks execute classification tasks. To address the "black-box" challenge in AI-assisted diagnosis, the framework incorporates quantum explainability modules using measurement-driven saliency mapping and Shapley value–based feature attribution. This dual- layer explainability provides radiologists with both a quantum- informed feature importance ranking and a visual heatmap overlay on the medical image. Experimental evaluation on publicly available medical imaging datasets demonstrates improved detection sensitivity (7.3%) and diagnostic confidence compared to conventional deep learning approaches, while reducing model opacity. The proposed XQAI system offers a pathway towards trustworthy, high-performance, and clinically deployable quantum-assisted diagnostic tools in radiology.
Modern healthcare systems increasingly rely on artificial intelligence, deep learning, and medical imaging technologies for accurate disease diagnosis and clinical decision-making. However, conventional AI systems still face challenges related to interpretability, computational complexity, centralized data dependency, and patient privacy. This paper presents an Explainable Quantum Deep Learning Framework integrating Vision Transformers, Federated Intelligence, and Explainable Artificial Intelligence techniques for smart healthcare applications. The proposed framework combines quantum-inspired optimization, multimodal medical data analysis, transformer-based feature extraction, and decentralized federated learning to improve diagnostic accuracy, scalability, privacy preservation, and interpretability. Explainability mechanisms including SHAP and Grad-CAM are incorporated to enhance physician trust and clinical transparency. Experimental evaluation demonstrates superior performance in biomedical image classification and disease prediction compared with conventional deep learning models, providing a scalable solution for next-generation intelligent healthcare systems.
Early and accurate prediction of cardiovascular disease is essential to improve patient outcomes and reduce healthcare costs. This research presents a hybrid classical–quantum machine learning framework for heart disease prediction using the Cleveland dataset. The proposed pipeline integrates advanced feature engineering, bio-inspired optimization, and quantum-inspired learning to improve classification performance and interpretability. The system applies multiple feature selection techniques followed by a hybrid feature fusion strategy. Orthogonal Component Analysis is then used for dimensionality transformation, while quantum-inspired feature mapping simulates quantum state coding. A feature selection mechanism based on a Genetic Algorithm optimizes the subset of features. Classical and quantum machine learning models are evaluated, including Random Forest, Gradient Boosting, K-Nearest Neighbors, Logistic Regression, Quantum Support Vector Classifier, Variational Quantum Classifier, Quantum KNN, and Quantum Neural Networks. Model performance is evaluated using accuracy metrics. To ensure transparency and trustworthiness, explainable AI techniques such as SHAP, LIME and DiCE are integrated to provide local and global interpretability of predictions. Experimental results demonstrate that the proposed hybrid framework improves predictive performance by achieving 90% accuracy compared to traditional machine learning approaches, while maintaining model explainability. The model achieved an overall accuracy of 90%, indicating strong predictive capability in cardiovascular disease risk classification. A detailed analysis of class-wise performance shows that for Class 0, the model obtained a precision of 0.85, a recall of 0.97, and an F1-score of 0.90, demonstrating excellent ability to correctly identify negative cases with minimal false negatives. For Class 1, the model achieved a precision of 0.96, a recall of 0.84, and an F1-score of 0.90, indicating high confidence in positive predictions, though with slightly lower recall compared to Class 0. This study highlights the potential of combining classical feature engineering, evolutionary optimization and quantum-inspired learning for next-generation medical decision support systems. The integration of quantum-inspired techniques also provides a promising direction for improving computational efficiency and model robustness in healthcare analytics. The findings suggest that hybrid classical–quantum learning approaches can support clinicians in making faster and more reliable diagnostic decisions.
Abstract Chronic disease prediction presents ongoing challenges in healthcare, primarily due to the complexity of medical data and the need for models that are both accurate and interpretable. This study introduces a quantum-enhanced machine learning model specifically designed for the prediction of hypertension, combining quantum feature transformation with classical algorithms to deliver precise and reliable results. The model demonstrates high performance, achieving an accuracy of 98.40%, precision of 99.3%, recall of 98.6%, and an F1-score of 98.9%. To ensure transparency and facilitate clinical interpretation, explainable AI (XAI) techniques are employed through SHAP values, highlighting critical factors such as hypertension drug usage, age, ferritin, and cholesterol levels as key contributors to hypertension prediction. This quantum-based approach exemplifies the potential for leveraging cutting-edge technologies in healthcare, offering a robust solution that not only ensures predictive accuracy but also supports interpretability—essential for informed clinical decision-making. The integration of quantum computing and explainable machine learning represents a promising step forward in the development of predictive models tailored to complex medical datasets.
The rapid growth of ultra-large and highdimensional datasets has intensified the demand for deep learning (DL) models that are scalable, accurate, transparent, and interpretable. In parallel, quantum-inspired computing has emerged as a promising paradigm that emulates quantum principles such as superposition, entanglement, and probabilistic state evolution without requiring quantum hardware. This study presents a comprehensive survey of explainable quantum-inspired DL models for large-scale data mining. Existing quantum-inspired architectures integrated with deep neural networks (DNN), including convolutional, recurrent, and attention-based models, are systematically analyzed alongside explainable artificial intelligence (XAI) techniques to enhance interpretability and trustworthiness. The reviewed methods are classified based on learning paradigms, explainability mechanisms, scalability strategies, and application domains such as healthcare analytics, cybersecurity, intelligent transportation, and industrial big data systems. Furthermore, evaluation metrics, computational complexity, and performance trade-offs are examined in comparison with conventional DL approaches. Finally, key open challenges and future research directions for real-world large-scale deployment are highlighted.