The quality of raw materials is fundamental to the reliability and overall performance of final products, serving as the cornerstone of modern manufacturing standards. While traditional inspection methods can be effective, they are frequently time-consuming, labor-intensive, and unsuitable for high-throughput production environments. Recent advances in quantum computing offer significant advantages for processing large-scale data and complex feature representations, with strong potential to address the limitations of traditional inspection techniques. This study introduces an automated approach for grading raw materials, such as steel and fabric, which are widely used in industries including automotive, textiles, and general manufacturing, by integrating deep learning with quantum machine learning. A Hybrid Quantum Classical Neural Network (HQCNN) is proposed to enhance defect detection. The HQCNN utilizes a fine-tuned ResNet-18 backbone for robust feature extraction. Extracted features are used by a trainable depth controller that determines the complexity of a six-qubit variational quantum circuit (VQC) with data-adaptive, dynamic depth. Each VQC layer consists of rotations around the Y -axis (RY) and Z -axis (RZ), combined with Controlled-NOT (CNOT) gates arranged in a ring topology, with the total number of layers determined on a per-sample basis. The model is trained using a resource-aware loss function that penalizes excessive circuit depth, and the resulting quantum features are fused with classical features for final classification. Comprehensive ablation studies comparing the HQCNN against parameter-matched classical multi-layer perceptrons (MLPs) and fixed-depth quantum circuits demonstrate the fundamental advantage of the proposed approach. The dynamic HQCNN achieves 88.1 ± 2.4% accuracy on the complex metal surface defect dataset and 93.0 ± 1.8% on the fabric defects dataset. More importantly, it yields a statistically significant improvement in the macro F1-score (from 0.79 in classical parameter-matched baselines to 0.87), proving its superior capability in recognizing complex, minority-class defects while utilizing a fraction of the trainable parameters in the classification head compared to traditional architectures.
The quality of raw materials is fundamental to the reliability and overall performance of final products, serving as the cornerstone of modern manufacturing standards. While traditional inspection methods can be effective, they are frequently time-consuming, labor-intensive, and unsuitable for high-throughput production environments. Recent advances in quantum computing offer significant advantages for processing large-scale data and complex feature representations, with strong potential to address the limitations of traditional inspection techniques. This study introduces an automated approach for grading raw materials, such as steel and fabric, which are widely used in industries including automotive, textiles, and general manufacturing, by integrating deep learning with quantum machine learning. A Hybrid Quantum Classical Neural Network (HQCNN) is proposed to enhance defect detection. The HQCNN utilizes a fine-tuned ResNet-18 backbone for robust feature extraction. Extracted features are used by a trainable depth controller that determines the complexity of a six-qubit variational quantum circuit (VQC) with data-adaptive, dynamic depth. Each VQC layer consists of rotations around the Y -axis (RY) and Z -axis (RZ), combined with Controlled-NOT (CNOT) gates arranged in a ring topology, with the total number of layers determined on a per-sample basis. The model is trained using a resource-aware loss function that penalizes excessive circuit depth, and the resulting quantum features are fused with classical features for final classification. Comprehensive ablation studies comparing the HQCNN against parameter-matched classical multi-layer perceptrons (MLPs) and fixed-depth quantum circuits demonstrate the fundamental advantage of the proposed approach. The dynamic HQCNN achieves 88.1 ± 2.4% accuracy on the complex metal surface defect dataset and 93.0 ± 1.8% on the fabric defects dataset. More importantly, it yields a statistically significant improvement in the macro F1-score (from 0.79 in classical parameter-matched baselines to 0.87), proving its superior capability in recognizing complex, minority-class defects while utilizing a fraction of the trainable parameters in the classification head compared to traditional architectures.
As manufacturing systems advance toward higher throughput, tighter tolerances, and increasingly complex quality requirements, traditional defect detection and quality assurance methods struggle to scale effectively. Quantum Machine Learning (QML) offers a new computational paradigm capable of capturing high-dimensional feature interactions, accelerating pattern recognition, and improving anomaly detection in industrial environments. This study investigates the integration of quantum kernel methods, variational quantum classifiers, and hybrid quantum–classical neural architectures for real-time defect identification across optical, acoustic, and sensor-rich inspection systems. The proposed framework leverages the expressivity of quantum feature spaces to enhance classification fidelity while maintaining compatibility with existing digital manufacturing pipelines. Experimental simulations demonstrate that QML models achieve notable improvements in detecting subtle surface irregularities, micro-defects, and non-linear process deviations when compared to conventional deep learning baselines. These findings underscore the potential of quantum-enhanced analytics to redefine quality assurance strategies in high-throughput manufacturing, enabling earlier defect localization, lower false-negative rates, and more resilient production workflows.
Given the recent prominence of Noisy Intermediate Scale Quantum (NISQ) devices, hybrid quantum-classical neural networks have recently drawn a lot of excitement as a potential solution to improving learning efficiency. Our current paper describes a Hybrid Quantum-Classical Convolutional Neural Network (HQCNN) which is defined as a pretrained backbone of ResNet-18 and a small quantum-enhanced feature-transformation layer of Qiskit. The convolutional high-dimensional features are reduced to the classical eight-dimensional one, which in turn is encoded into a four-qubit parameterized quantum circuit. The resultant quantum expectation values are combined with the compressed features to come up with a hybridized representation of twelve dimensions, used to classify multi-class dataset. Experimental analysis of CIFAR10 data has shown that the suggested HQCNN achieves a test accuracy of 94.74% and improves class-wise precision, recall, and F1-scores than a number of existing hybrid models while using a significantly smaller number of latent features. More investigation into the quantum module indicates that it is predictable about scalability to batch, resistant to perturbations in features, and efficiently performed by evaluating circuit vectors. These results indicate that compact and expressive representations of images that can be helpful in the NISQ era can be acquired using hybrid architectures, thus providing a practical way to forward the benefits of quantum to modern deep-learning pipelines.
Medical image classification is a critical component of modern healthcare; however, accurate diagnosis remains challenging due to limited annotated datasets, class imbalance, and the high dimensionality of medical imaging data. To address these challenges, a hybrid quantum-classical neural network (HQCNN) is proposed, integrating classical deep learning with variational quantum learning for medical image classification. The proposed architecture combines a five-layer convolutional neural network (CNN) for hierarchical feature extraction with a lightweight 4-qubit variational quantum circuit (VQC) incorporating quantum state encoding, superposition and entanglement mechanisms, and a quantum attention-Fourier (QAF) module. This hybrid design aims to improve nonlinear feature representation and quantum parameter efficiency while maintaining a shallow quantum circuit suitable for noisy intermediate-scale quantum (NISQ)-era constraints. Experimental evaluation on six MedMNIST benchmark datasets demonstrated competitive performance across both binary and multi-class classification tasks. HQCNN achieved 98.88% accuracy on the binary subset of PathMNIST (classes 0 vs. 1), 97.61% accuracy on the multi-class OrganAMNIST dataset, and 86.29% accuracy on BreastMNIST. Comparative experiments and statistical analyses demonstrated consistent improvements over the controlled BHQNN baseline, while component-wise ablation studies showed that the QAF module, superposition and entanglement mechanisms, and expressive parameterized rotations contributed to classification performance in a complementary and dataset-dependent manner. Moreover, HQCNN reduced the number of trainable quantum parameters by approximately 55.6% compared with the baseline hybrid quantum neural network (BHQNN). Noise-aware simulations further showed that the model retained relatively stable predictive performance under moderate depolarizing noise, supporting further evaluation under near-term quantum computing conditions. Overall, the results demonstrate that HQCNN provides a parameter-efficient hybrid quantum-classical framework for medical image classification and offers a promising foundation for further investigation of quantum-enhanced medical image analysis.
Quantum computing, based on fundamental quantum-mechanical principles and phenomena such as superposition, entanglement, and interference, offers access to high-dimensional quantum feature spaces and complex correlations that may be difficult to efficiently represent using classical computing, along with potential computational advantages through quantum hardware. Therefore, quantum computing has emerged as a promising paradigm for addressing limitations of classical deep learning (DL) through fundamentally different computational mechanisms. Quantum computing-based DL approaches have been applied to image classification; however, most rely on existing quantum computing libraries that impose substantial computational costs, limiting their broader applicability. In this study, hybrid quantum convolutional neural networks (HQCNNs) are proposed for structural damage classification without relying on existing quantum computing libraries. The proposed architectures integrate quanvolutional layers, composed of encoding, variational quantum circuits, and measurement, with conventional convolutional layers in a CNN architecture, enabling quantum-enhanced feature extraction while maintaining compatibility with classical computing systems. The scratch-implemented HQCNN (HQCNN(S)), its library-based counterpart (HQCNN(P)), and the classical CNN are compared in terms of test accuracy, computational cost, and training efficiency. Experimental results demonstrate that HQCNN(S) ( at ) achieves up to 176.57 faster computation than HQCNN(P) and 3.1% higher test accuracy than the classical CNN, while reducing the number of trainable parameters by 60.2%. Moreover, HQCNN(S) achieves comparable performance to the best-performing classical CNN using 75% less training data.
The economic value of predictive maintenance (PdM) for industrial IoT machinery is undeniable, as unplanned equipment downtime is estimated to cost industries USD 50 billion a year worldwide. Deep learning techniques have achieved good fault classification results on benchmark datasets, but they are not robust enough to cope with noise in industrial environments, are computationally intensive for use at the edge, and are unable to make good use of the capabilities of quantum computing. In this paper, a new hybrid network, called QEML-Net (Quantum-Enhanced Machine Learning Network), is proposed to combine the ResNet-50 deep residual network with Convolutional Block Attention Modules (CBAM), variational quantum feature enhancement, and a compound hybrid loss function, for efficient fault diagnosis. The framework features a 6-layer, 4-qubit Parameterized Quantum Circuit (PQC) with angle encoding and linear CNOT entanglement that is implemented using PennyLane. Experiments were performed on six harmonized datasets from public PdM which have 57164 samples across eight unified fault categories. The framework was validated with the help of Bayesian hyperparameter optimization, 5-fold stratified cross-validation, ablation studies, and statistical testing. By optimizing the deployment on the edges, QEML-Net got 97.3% accuracy, 96.9% macro-F1 score, AUC of 0.988, and real-time inference performance with 19ms latency and 52FPS throughput on the benchmark dataset. The statistical analysis revealed significant improvement when compared to other methods (p < 0.001). Even when evaluated cross-dataset without fine-tuning, good generalization was achieved with an accuracy of 92.8–94.6%. The results reveal the advantages of combining the quantum variational circuits with deep learning to enhance the classification accuracy, interpretability, and deployment efficiency for the fault diagnosis of industrial IoT.
Accurate classification of microscopic blood cells is still a critical task in medical image analysis, where subtle variations and limited data can challenge conventional deep learning models. As such, we investigate in this work the potential of Hybrid Quantum-Classical Neural Networks (HQNNs) to enhance feature representation and improve classification performance in this domain. We propose a modular architecture combining a pre-trained ResNet-50 backbone with a low-dimensional latent bottleneck and a variational quantum circuit, enabling a direct comparison between quantum-enhanced and purely classical transformation mechanisms. To isolate the contribution of the quantum component, we evaluate three architectures: a HQNN model, a Classical Matched Model with an additional nonlinear transformation layer of comparable capacity, and a baseline model without an intermediate transformation stage. Experiments conducted on two publicly available blood cell datasets, namely the Blood Cell Images dataset and the PBC dataset, demonstrate that HQNNs consistently achieve superior or more balanced performance across evaluation metrics. In the Blood Cell Images Dataset, the proposed approach improves macro F1-score by up to 3.7% compared to classical baselines, while improving the F1-score from 98.54% to 98.69% in the more challenging 8-class scenario with near-saturated performance. Additional evaluation on IBM quantum hardware shows that the model remains robust under noise, with only a modest performance degradation relative to simulated results. These results indicate that quantum feature transformations can enhance discriminative representations, particularly in challenging classification scenarios, and highlight the practical potential of HQNN models for medical imaging tasks.