Quantum Machine Learning for Defect Detection and Quality Assurance in High-Throughput Manufacturing

Abstract

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.