Hybrid Quantum-Classical AI for Industrial Defect Classification in Welding Images
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. ...
| Authors | Akshaya Srinivasan, Xiaoyin Cheng, Jianming Yi et al. |
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| Primary category | cs.CV |
| Published | 30 March 2026 |
| Last updated at source | 30 March 2026 |
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