Hybrid quantum-classical machine learning offers a promising direction for advancing automated quality control in industrial settings. In this study, we investigate two hybrid quantum-classical approaches for classifying defects in aluminium TIG welding images and benchmarking their performance against a conventional deep learning model. A convolutional neural network is used to extract compact and informative feature vectors from weld images, effectively reducing the higher-dimensional pixel space to a lower-dimensional feature space. Our first quantum approach encodes these features into quantum states using a parameterized quantum feature map composed of rotation and entangling gates. We compute a quantum kernel matrix from the inner products of these states, defining a linear system in a higher-dimensional Hilbert space corresponding to the support vector machine (SVM) optimization problem and solving it using a Variational Quantum Linear Solver (VQLS). We also examine the effect of the quantum kernel condition number on classification performance. In our second method, we apply angle encoding to the extracted features in a variational quantum circuit and use a classical optimizer for model training. Both quantum models are tested on binary and multiclass classification tasks and the performance is compared with the classical CNN model. Our results show that while the CNN model demonstrates robust performance, hybrid quantum-classical models perform competitively. This highlights the potential of hybrid quantum-classical approaches for near-term real-world applications in industrial defect detection and quality assurance.
Figures & tables
Figure 1: Full architecture of the study, showing CNN-based feature extraction followed by three classification approaches: classical CNN, VQLS-enhanced QSVM, and VQC-based classifier.
Figure 2: CNN architecture for Feature Extraction. The last fully connected layer outputs a feature vector X∈{7,15,31,63,127} .
Figure 3: Classical CNN architecture for classification.
Figure 4: Pipeline for VQLS-enhanced QSVM algorithm.
Figure 5: Quantum circuit for hardware-efficient ansatz.
Figure 6: Pipeline for VQC-based classifier algorithm for binary classification.
Figure 7: Six class categorization of welding defects: a) Good Weld, b) Burn through, c) Contamination, d) Lack of fusion, e) Misalignment, and f) Lack of penetration. These images are taken from Kaggle [ 20 ] .
Figure 8: Three class categorization of welding defects: a) Good Weld, b) Contamination, and c) Lack of fusion.
Figure 9: Hyperparameter tuning for the VQC-based classifier over feature sizes (7, 15, 31, 63, 127) and qubit configurations (4, 7, 12, 15), with a fixed batch size of 16.
Feature size
7
15
31
63
127
Accuracy
1.000
1.000
1.000
1.000
1.000
Loss
0.0204
0.0066
0.0145
0.0020
0.0003
Table 1: Binary classification results using the classical CNN model on the test set (400 images).
Feature size
7
15
31
63
127
Accuracy
0.753
0.856
0.871
0.968
0.948
Loss
0.454
0.395
0.341
0.201
0.238
Table 2: Binary classification results using the VQLS-enhanced QSVM model on the test set (400 images).
Feature size
7
15
31
63
127
Accuracy
0.915
0.876
0.925
0.997
0.951
Loss
0.126
0.235
0.164
0.042
0.113
Table 3: Binary classification results using the VQC-based classifier model on the test set (400 images).
Figure 10: Confusion matrices for binary classification at feature size 63: ( a ) VQLS-enhanced QSVM, ( b ) VQC-based classifier.
Feature size
7
15
31
63
127
Accuracy
1.000
0.9925
0.9975
1.000
0.9975
Loss
0.0326
0.0288
0.0056
0.0011
0.0309
Table 4: Multiclass classification results using the classical CNN model on the test set (400 images).
Feature size
7
15
31
63
127
Accuracy
0.625
0.700
0.772
0.924
0.884
Loss
0.684
0.482
0.437
0.359
0.384
Table 5: Multiclass classification results using the VQLS-enhanced QSVM model on the test set (400 images).
Feature size
7
15
31
63
127
Accuracy
0.890
0.892
0.942
0.989
0.946
Loss
0.203
0.209
0.126
0.107
0.115
Table 6: Multiclass classification results using the VQC-based classifier model on the test set (400 images).
Figure 11: Confusion matrices for multiclass (3-class) classification at feature size 63: ( a ) VQLS-enhanced QSVM, ( b ) VQC-based classifier.
Figure 12: Visual cues influencing classification results. a) Good weld with reflection head resembling contamination, and b) Lack of Fusion defect with dark line in the center aiding detection.
Extracting actionable knowledge from industrial visual data is fundamentally bottlenecked by extreme class imbalance and the prohibitive computational complexity of modern foundation models. In semi-conductor manufacturing, identifying multi-label wafer defects is a complex spatial data mining task where overlapping patterns obscure critical root-cause signals. While Vision Transformers (ViTs) excel at global dependency extraction, their quadratic scaling renders them inefficient for high-throughput, real-time anomaly detection. To overcome these computational barriers, this paper introduces Hybrid Quantum-MambaVision, a highly efficient architecture tailored for spatial knowledge discovery. We integrate a linear-complexity State-Space Model (SSM) backbone with a Parameterized Quantum Context Adapter (QCA) and Low-Rank Adaptation (LoRA). The Mamba backbone efficiently captures long-range spatial dependencies, while the quantum adapter maps compressed latent features into a high-dimensional Hilbert space to disentangle complex, overlapping signatures. On the highly imbalanced MixedWM38 dataset, Hybrid Quantum-MambaVision achieves exceptional multi-label classification performance, significantly reducing the error rate on complex multi-defect topologies compared to classical baselines. The quantum regularizer acts as a profound uncertainty calibrator, substantially reducing Maximum Calibration Error (MCE) and minimizing expected false-positive costs. This work establishes a scalable Quantum-Classical hybrid paradigm for efficient representation learning in industrial data mining.
Satwik Sai Prakash Sahoo, Jyoti Prakash Sahoo, Ting Wang +1
Odisha University of Technology and Research, Bhubaneswar, India · National Taiwan University of Science and Technology, Taipei, Taiwan · East China Normal University, Shanghai, China +1
The integration of quantum machine learning with classical deep learning offers promising avenues for medical image analysis by mapping data into high-dimensional Hilbert spaces. However, effectively unifying these distinct paradigms remains challenging due to common optimization asymmetries. In this paper, a novel hybrid quantum-classical architecture for breast cancer diagnosis based on a dual-branch feature-extraction pipeline is proposed. Our framework extracts and unifies complementary representations from classical models and quantum circuits, exploring both trainable and deterministic (non-trainable) quantum paradigms. To integrate these embeddings, three progressive feature fusion strategies are introduced: Static Hybrid Fusion (SHF) for offline extraction, Dynamic Hybrid Fusion (DHF) for end-to-end co-adaptation, and a novel Temperature-Scaled Hybrid Fusion (TSHF). The TSHF strategy incorporates a learnable scalar, inspired by multimodal learning, that dynamically balances hybrid gradient dynamics and resolves optimization bottlenecks. Empirical validation on the BreastMNIST dataset confirms our hypothesis that unifying diverse feature representations creates a richer data context. The TSHF strategy, specifically when pairing a ResNet backbone with a trainable quantum circuit, achieved a peak accuracy of 87.82%, F1-score of 91.77%, and an AUC-ROC of 89.08%, outperforming purely classical baselines. These results demonstrate that the proposed hybrid framework improves classification accuracy and threshold reliability, providing a stable, high-performance architecture for the clinical deployment of quantum-enhanced diagnostic tools.
Yasmin Rodrigues Sobrinho, João Renato Ribeiro Manesco, João Paulo Papa
Department of Computing, São Paulo State University, Ave. Engenheiro Luiz Edmundo Carrijo Coube, 14-01 - Vargem Limpa, Bauru, 17033-360, São Paulo, Brazil
Breast cancer diagnosis through thermographic image analysis remains a critical challenge in medical AI, with classical deep learning approaches facing limitations in complex thermal pattern classification tasks. This paper presents a novel Hybrid Quantum Neural Network (HQNN) architecture that integrates quantum computing principles with classical convolutional neural networks for enhanced breast cancer classification. Our approach employs parameterized quantum circuits with multi-head attention mechanisms for quantum-aware feature encoding, coupled with classical convolutional layers for comprehensive pattern recognition. The quantum component utilizes a 4qubit variational circuit with strongly entangling layers, while the classical component incorporates advanced attention mechanisms for feature fusion. Experimental validation on breast cancer thermographic data demonstrates substantial performance improvements over state-of-the-art classical architectures, with the quantum-enhanced approach exhibiting superior convergence dynamics and enhanced feature representation capabilities. Our findings provide evidence for quantum advantage in medical image classification through classical simulation, establishing a framework for quantum-classical hybrid systems in healthcare applications. The methodology addresses key challenges in quantum machine learning deployment while maintaining computational feasibility on near-term quantum devices.
Riza Alaudin Syah, Irwan Alnarus Kautsar, Gunawan Witjaksono +1
Faculty of Computing Universiti Teknologi Malaysia Johor Bahru, Malaysia · Informatics Department Faculty of Science and Technology Universitas Muhammadiyah Sidoarjo Sidoarjo, Indonesia · Department of Information System Universitas Siber Jakarta Jakarta, Indonesia