Hybrid Quantum-Classical AI for Industrial Defect Classification in Welding Images

View PDF HTML (experimental)

Abstract: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.

Subjects:

Computer Vision and Pattern Recognition (cs.CV); Image and Video Processing (eess.IV); Quantum Physics (quant-ph)

Cite as: arXiv:2603.28995 [cs.CV]

(or arXiv:2603.28995v1 [cs.CV] for this version)

https://doi.org/10.48550/arXiv.2603.28995

arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Akshaya Srinivasan [view email] [v1] Mon, 30 Mar 2026 20:48:51 UTC (6,764 KB)