摘要：AbstractThe aim of this paper is to explore the potential capabilities of quantum machine learning technology (a branch of quantum computing) when applied to surface quality supervision inside steel manufacturing processes where environmental conditions can affect the quality of images. Comparison with classical deep learning classification schema is performed. The application case, driven by the so-called quantvolutional configuration, shows a large potential of using this technology in this field, mainly because of the speed when using a physical quantum engine.
关键词：KeywordsSteel DescalerQuality of Steel BilletQuantum Deep LearningQuantvolutional Neural NetworkDeep LearningQuality in Steel Industry
