Early transformer fault diagnosis is challenged by nonlinear dissolved-gas interactions, overlapping fault signatures, and limited labeled data, while practical deployment further requires reliable performance under realistic computational constraints. This paper presents a simulation-driven modeling framework for dissolved gas analysis-based transformer fault diagnosis, in which a carefully engineered variational quantum classifier (VQC) is employed as the computational core and systematically analyzed through simulation. The framework integrates domain-aware feature modeling derived from Duval geometry with a lightweight two-qubit quantum representation, enabling nonlinear gas-interaction effects to be captured within a shallow parameterized circuit. A hybrid ZX-YY quantum feature map is designed to model non-commuting feature interactions, while a full-entanglement EfficientSU2 ansatz provides adequate expressive capacity under strict resource limits. Model behavior is evaluated using a comprehensive simulation pipeline including noise-aware circuit emulation, cross-dataset validation, and limited hardware-in-the-loop execution, allowing key effects of circuit depth, noise, and optimization strategy to be examined. Simulation results on benchmark dissolved-gas-analysis datasets demonstrate high diagnostic accuracy, strong generalization capability, and robustness to realistic noise levels with minimal quantum resources. The results highlight the effectiveness of simulation-informed modeling for practical transformer diagnostic applications, offering a reproducible and resource-efficient pathway for evaluating quantum-enhanced fault diagnosis methods.
Journal reference
Alexandria Engineering Journal. Volume 144, May 2026, Pages 58-78
Early transformer fault diagnosis is challenged by nonlinear dissolved-gas interactions, overlapping fault signatures, and limited labeled data, while practical deployment further requires reliable performance under realistic computational constraints. This paper presents a simulation-driven modeling framework for dissolved gas analysis-based transformer fault diagnosis, in which a carefully engineered variational quantum classifier (VQC) is employed as the computational core and systematically analyzed through simulation. The framework integrates domain-aware feature modeling derived from Duval geometry with a lightweight two-qubit quantum representation, enabling nonlinear gas-interaction effects to be captured within a shallow parameterized circuit. A hybrid ZX-YY quantum feature map is designed to model non-commuting feature interactions, while a full-entanglement EfficientSU2 ansatz provides adequate expressive capacity under strict resource limits. Model behavior is evaluated using a comprehensive simulation pipeline including noise-aware circuit emulation, cross-dataset validation, and limited hardware-in-the-loop execution, allowing key effects of circuit depth, noise, and optimization strategy to be examined. Simulation results on benchmark dissolved-gas-analysis datasets demonstrate high diagnostic accuracy, strong generalization capability, and robustness to realistic noise levels with minimal quantum resources. The results highlight the effectiveness of simulation-informed modeling for practical transformer diagnostic applications, offering a reproducible and resource-efficient pathway for evaluating quantum-enhanced fault diagnosis methods.
Lung cancer remains the leading cause of cancer-related mortality worldwide, with accurate subtype classification being critical for improving patient outcomes. Vision Transformers (ViTs) have recently advanced medical image analysis, but their integration with quantum computing remains underexplored, leaving a gap in leveraging quantum advantages for clinical applications. This study proposes a quantum-enhanced Vision Transformer (QViT) that embeds variational quantum circuits (VQCs) within attention and feed-forward layers to enrich feature learning. To assess robustness, two circuit families, Basic VQC and Quantum Approximate Optimization Algorithm (QAOA) based VQC, were evaluated across two depths, yielding four QViT configurations. The models were analyzed through cancer classification tasks, systematic quantum state tomography (QST) to examine quantum state properties, and noise resilience testing under realistic Noisy Intermediate-Scale Quantum (NISQ) conditions, reflecting current hardware limitations. On 3150 CT (Computed Tomography) images, the QViT-QAOA-D1 configuration achieved 98.52% accuracy with a training time of 674 s, outperforming other configurations. Quantum analyses further showed that Basic VQC provides stronger entanglement but requires deeper, less NISQ-friendly circuits, whereas QAOA maintains high-purity states at shallower depths. These results suggest that QViT provides a practical and scalable framework, where quantum-enhanced representations can improve diagnostic performance while remaining compatible with current hardware constraints. Future work will focus on validation using patient-level datasets and deployment on real quantum hardware to further assess clinical applicability and scalability.
An improved variational quantum shadow learning (VQSL) method is presented for the fault diagnosis of transformers. Localized variational quantum circuits for shadow feature extraction and a fully connected neural network are designed. Firstly, the dissolved gas analysis data is mapped into a high-dimensional Hilbert space, then, shadow features between adjacent and non-adjacent quantum bits are extracted simultaneously by multiple localized quantum circuits through parameter sharing, which reduces the number of training parameters and improves the model accuracy. The key factors that influence the performance of the VQSL method are discussed, which include quantum coding methods, the width and the depth of localized quantum circuits, the size of the global quantum bits, and the noise levels. Experimental results indicate that the improved VQSL method achieves a diagnostic accuracy of 95.4%, surpassing the commonly used methods such as QNN, GNN, CNN, SAE, SVM, and ratio methods. Even in a noise interference situation, the proposed method consistently maintains a high fault diagnosis accuracy.
Automated epileptic seizure detection from electroencephalogram (EEG) recordings remains a challenging biomedical signal analysis task because of the nonlinear, non-stationary, and high-dimensional characteristics of neural activity. Although deep learning has substantially improved automated seizure classification, conventional architectures may exhibit limited capability in modeling complex latent feature interactions while maintaining computational efficiency in biomedical applications. This study presents an entanglement-driven hybrid quantum–classical representation learning framework that integrates Continuous Wavelet Transform (CWT)-based time–frequency analysis, EfficientNet-B0 feature extraction, quantum-compatible latent feature compression, angle-based quantum state encoding, and a customized four-qubit Variational Quantum Convolutional Neural Network (QCNN) for EEG seizure classification. The proposed shallow variational quantum architecture is designed to facilitate expressive quantum feature learning while remaining compatible with the computational constraints of near-term noisy intermediate-scale quantum (NISQ) systems. An experimental evaluation was conducted on a balanced subset of 4,000 EEG segments from the UCI Epileptic Seizure Recognition Dataset. Stratified five-fold cross-validation achieved a mean classification accuracy of 97.80% ± 1.50%, demonstrating consistent predictive performance and generalization across several data partitions. Furthermore, evaluation on an independent held-out test set yielded 99.50% accuracy, 99.03% precision, 100.00% recall, 99.51% F1-score, and an area under the ROC curve (AUC) of 1.000. To comprehensively assess the proposed framework, additional investigations, including quantum circuit expressibility, barren plateau analysis, optimization trainability, latent feature-space separability, robustness evaluation, computational scalability, and patch-level quantum attention visualization, were performed, offering complementary evidence regarding representation quality, optimization stability, and model interpretability. Although the proposed framework is currently validated using classical quantum simulation, the results demonstrate the feasibility of hybrid quantum–classical representation learning for intelligent EEG seizure analysis and establish a foundation for future deployment on emerging quantum-computing platforms.
Although automated diagnosis in medical imaging has made tremendous breakthroughs with deep learning, current models also exhibit difficulties with representation stability, calibration, and robustness to domain heterogeneity. Traditional methods that use deeper CNN or transformer networks incur greater computational costs without fully addressing feature separability and uncertainty estimation. To address these shortcomings, the present study proposes a hybrid quantum-classical approach, VQMedNet, that integrates variational quantum circuits with classical feature extraction for supervised classification of MRI and CT images. The suggested QFeatEmbed-VQC module is an NISQ-conscious nonlinear quantum feature embedding that can be trained on tokenised and compressed image features, enabling effective learning with constrained quantum resources. A gradual training approach is followed to achieve consistent optimisation, and calibration-sensitive assessment is included to measure the predictability. The experiments with the brain tumour MRI (BraTS) and lung CT (LIDC-IDRI) datasets show a stable increase over the classical baseline, achieving 95.6% accuracy and 0.947 Macro-F1 on MRI classification, and 93.1% accuracy with improved AUC-PR on CT tasks. Additional analyses show enhanced embedding separability, robustness to domain perturbations, and reduced Expected Calibration Error. Moreover, the findings suggest that moderate-depth quantum circuits offer a good balance between performance and computational complexity. Although tested under simulation-based conditions, the proposed framework can indicate the potential of quantum-enhanced feature embedding for building stable and efficient medical AI systems. It may be seen as a promising approach to future NISQ-aware diagnostics. The implementation is publicly available at: https://github.com/gvvsrk/QuantumMed-FedDx . Clinical trial number: Not applicable.
Heart disease is one of the major global causes of death, requiring early and precise detection for efficient treatment.With advancements in quantum computing, Variational Quantum Circuits (VQCs) have gained recognition as a valuable technique for improving learning models in medical diagnosis.This study further explores the application of VQC-based models for heart disease prediction, leveraging quantum computing to enhance classification accuracy.A hybrid quantum-classical model has been developed, which combines classical preprocessing for feature extraction from patient datasets with a VQC-based classifier for medical predictions.The quantum circuit using variational parameters is optimized to reduce the loss function and facilitate effective learning from complex medical data.This proposed approach is tested on benchmark heart disease datasets and evaluated against classical Machine Learning (ML) models, including Support Vector Machines (SVMs), Random Forests, and Neural Networks.The experimental results show that VQC-based models achieve high accuracy and outperform classical models, notably on high-dimensional, non-linearly separable data.The quantum advantage of VQCs lies in their ability to encode and process data in a huge Hilbert space.However, challenges such as noise in near-term quantum devices and scalability issues remain key.This research underscores the efficacy of VQCbased quantum machine learning in medical diagnosis, paving the way for future studies on quantum-enhanced healthcare solutions.Further advancements in quantum hardware and hybrid learning techniques could allow quantum-assisted heart disease prediction to become an effective clinical tool.
Noisy Intermediate-Scale Quantum (NISQ) devices can be used to solve classification problems using a promising quantum machine learning technique called Variational Quantum Classifiers (VQC). The performance of VQCs is highly dependent on classical optimizers, and the understanding of the optimizer behavior within a given quantum architecture is limited. The proposed work is to present an Adaptive Optimizer-Enhanced Variational Quantum Classifier, which is a combination of a ZZFeatureMap for quantum data encoding and TwoLocal ansatz for variational learning, for solving non-linear binary classification problems. The proposed framework is tested using the Two Moons benchmark dataset, which is a popular benchmark for evaluating the ability of machine learning models to learn complex non-linear decision boundaries. The implementation is based on the Qiskit 1.2 and was run on a noiseless state vector simulator to remove the effects of hardware noise and focus on the effects of optimization. The three popular optimization techniques, namely ADAM, COBYLA and SPSA were explored under the same experimental setups. Besides the classification accuracy, a callback-based loss tracking mechanism was added to examine the speed of convergence, stability of optimization and evolution of losses during training. The experimental results showed that the overall accuracy of ADAM is 84.2%, which is the best, followed by COBYLA (79.3%) and SPSA (73.8%). Along with this, quantitative convergence analysis results showed that ADAM converged faster than the other optimizers and had lesser loss variance. It is shown that, with a proper optimization routine, a shallow two-qubit VQC can be used to successfully learn complex non-linear decision boundaries. The principal contribution of this work is a controlled optimizer-aware evaluation framework that integrates a fixed ZZFeatureMap–TwoLocal VQC architecture with callback-based convergence diagnostics to systematically analyse optimizer behaviour under identical experimental conditions.