{"title":"针对不锈钢耐腐蚀性的量子机器学习","authors":"Muhamad Akrom , Supriadi Rustad , Totok Sutojo , De Rosal Ignatius Moses Setiadi , Hermawan Kresno Dipojono , Ryo Maezono , Moses Solomon","doi":"10.1016/j.mtquan.2024.100013","DOIUrl":null,"url":null,"abstract":"<div><p>This study evaluates the efficacy of quantum machine learning (QML) models in predicting stainless steel corrosion behaviour. Using two datasets, the quantum support vector classifier (QSVC) outperformed classical models, achieving accuracies of 95.46 % and 94.80 % for Dataset A and Dataset B, respectively. The QSVC excelled in identifying complex corrosion classes and demonstrated robust performance across diverse environments. This QML approach accurately predicts corrosion without experimental testing, saving significant time and cost. Future research will aim to include more environmental variables and steel types, broadening the model's applicability.</p></div>","PeriodicalId":100894,"journal":{"name":"Materials Today Quantum","volume":"3 ","pages":"Article 100013"},"PeriodicalIF":0.0000,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2950257824000131/pdfft?md5=2dbe1782598f260eba88f00b35e603ac&pid=1-s2.0-S2950257824000131-main.pdf","citationCount":"0","resultStr":"{\"title\":\"Quantum machine learning for corrosion resistance in stainless steel\",\"authors\":\"Muhamad Akrom , Supriadi Rustad , Totok Sutojo , De Rosal Ignatius Moses Setiadi , Hermawan Kresno Dipojono , Ryo Maezono , Moses Solomon\",\"doi\":\"10.1016/j.mtquan.2024.100013\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>This study evaluates the efficacy of quantum machine learning (QML) models in predicting stainless steel corrosion behaviour. Using two datasets, the quantum support vector classifier (QSVC) outperformed classical models, achieving accuracies of 95.46 % and 94.80 % for Dataset A and Dataset B, respectively. The QSVC excelled in identifying complex corrosion classes and demonstrated robust performance across diverse environments. This QML approach accurately predicts corrosion without experimental testing, saving significant time and cost. Future research will aim to include more environmental variables and steel types, broadening the model's applicability.</p></div>\",\"PeriodicalId\":100894,\"journal\":{\"name\":\"Materials Today Quantum\",\"volume\":\"3 \",\"pages\":\"Article 100013\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-09-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.sciencedirect.com/science/article/pii/S2950257824000131/pdfft?md5=2dbe1782598f260eba88f00b35e603ac&pid=1-s2.0-S2950257824000131-main.pdf\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Materials Today Quantum\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S2950257824000131\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Materials Today Quantum","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2950257824000131","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
摘要
本研究评估了量子机器学习（QML）模型在预测不锈钢腐蚀行为方面的功效。利用两个数据集，量子支持向量分类器（QSVC）的表现优于经典模型，数据集 A 和数据集 B 的准确率分别达到 95.46 % 和 94.80 %。QSVC 在识别复杂的腐蚀类别方面表现出色，并在不同环境下表现出稳健的性能。这种 QML 方法无需实验测试即可准确预测腐蚀，从而节省了大量时间和成本。未来的研究将致力于纳入更多的环境变量和钢材类型，从而扩大模型的适用性。
Quantum machine learning for corrosion resistance in stainless steel
This study evaluates the efficacy of quantum machine learning (QML) models in predicting stainless steel corrosion behaviour. Using two datasets, the quantum support vector classifier (QSVC) outperformed classical models, achieving accuracies of 95.46 % and 94.80 % for Dataset A and Dataset B, respectively. The QSVC excelled in identifying complex corrosion classes and demonstrated robust performance across diverse environments. This QML approach accurately predicts corrosion without experimental testing, saving significant time and cost. Future research will aim to include more environmental variables and steel types, broadening the model's applicability.
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