{"title":"用于工业生产预测分析的变分量子分类器","authors":"Antimo Angelino , Enrico Landolfi , Alfredo Massa , Alfredo Troiano","doi":"10.1016/j.iot.2025.101695","DOIUrl":null,"url":null,"abstract":"<div><div>Quantum Computing (QC) is a novel and disruptive paradigm of computation that leverages the properties of quantum mechanical systems to represent and process information. The interest in this emerging technology and its applications has been growing in recent years, especially regarding Quantum Machine Learning (QML). In QML, QC and Machine Learning (ML) techniques are combined to build more powerful and accurate learning models. Industries and research centers worldwide have been devoting significant efforts to find use cases of practical interest for which QML may be a suitable approach. In this work, one of the most common QML algorithms, namely a Variational Quantum Classifier (VQC), has been adopted for a supervised classification task in defence industry. The goal is to predict the failures that may happen during the final acceptance test of a finished product, based on the knowledge of test data related to its subassemblies. The test data have been collected using advanced IoT systems and the prediction has been made before the final product was assembled, so to improve the efficiency in the testing process. The VQC has been applied to a problem already approached with classical ML techniques, and then the classical and quantum performances have been compared. The results indicate promising performances and highlight the potential of QML algorithms in the industrial sector for predictive analysis use.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"33 ","pages":"Article 101695"},"PeriodicalIF":7.6000,"publicationDate":"2025-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A Variational Quantum Classifier for predictive analysis in industrial production\",\"authors\":\"Antimo Angelino , Enrico Landolfi , Alfredo Massa , Alfredo Troiano\",\"doi\":\"10.1016/j.iot.2025.101695\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>Quantum Computing (QC) is a novel and disruptive paradigm of computation that leverages the properties of quantum mechanical systems to represent and process information. The interest in this emerging technology and its applications has been growing in recent years, especially regarding Quantum Machine Learning (QML). In QML, QC and Machine Learning (ML) techniques are combined to build more powerful and accurate learning models. Industries and research centers worldwide have been devoting significant efforts to find use cases of practical interest for which QML may be a suitable approach. In this work, one of the most common QML algorithms, namely a Variational Quantum Classifier (VQC), has been adopted for a supervised classification task in defence industry. The goal is to predict the failures that may happen during the final acceptance test of a finished product, based on the knowledge of test data related to its subassemblies. The test data have been collected using advanced IoT systems and the prediction has been made before the final product was assembled, so to improve the efficiency in the testing process. The VQC has been applied to a problem already approached with classical ML techniques, and then the classical and quantum performances have been compared. The results indicate promising performances and highlight the potential of QML algorithms in the industrial sector for predictive analysis use.</div></div>\",\"PeriodicalId\":29968,\"journal\":{\"name\":\"Internet of Things\",\"volume\":\"33 \",\"pages\":\"Article 101695\"},\"PeriodicalIF\":7.6000,\"publicationDate\":\"2025-07-16\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Internet of Things\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S2542660525002094\",\"RegionNum\":3,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"COMPUTER SCIENCE, INFORMATION SYSTEMS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Internet of Things","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2542660525002094","RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, INFORMATION SYSTEMS","Score":null,"Total":0}
A Variational Quantum Classifier for predictive analysis in industrial production
Quantum Computing (QC) is a novel and disruptive paradigm of computation that leverages the properties of quantum mechanical systems to represent and process information. The interest in this emerging technology and its applications has been growing in recent years, especially regarding Quantum Machine Learning (QML). In QML, QC and Machine Learning (ML) techniques are combined to build more powerful and accurate learning models. Industries and research centers worldwide have been devoting significant efforts to find use cases of practical interest for which QML may be a suitable approach. In this work, one of the most common QML algorithms, namely a Variational Quantum Classifier (VQC), has been adopted for a supervised classification task in defence industry. The goal is to predict the failures that may happen during the final acceptance test of a finished product, based on the knowledge of test data related to its subassemblies. The test data have been collected using advanced IoT systems and the prediction has been made before the final product was assembled, so to improve the efficiency in the testing process. The VQC has been applied to a problem already approached with classical ML techniques, and then the classical and quantum performances have been compared. The results indicate promising performances and highlight the potential of QML algorithms in the industrial sector for predictive analysis use.
期刊介绍:
Internet of Things; Engineering Cyber Physical Human Systems is a comprehensive journal encouraging cross collaboration between researchers, engineers and practitioners in the field of IoT & Cyber Physical Human Systems. The journal offers a unique platform to exchange scientific information on the entire breadth of technology, science, and societal applications of the IoT.
The journal will place a high priority on timely publication, and provide a home for high quality.
Furthermore, IOT is interested in publishing topical Special Issues on any aspect of IOT.