Variational quantum circuit-based quantum machine learning approach for predicting corrosion inhibition efficiency of pyridine-quinoline compounds
This work used a variational quantum circuit (VQC) in conjunction with a quantitative structure-property relationship (QSPR) model to completely investigate the corrosion inhibition efficiency (CIE) displayed by pyridine-quinoline compounds acting as corrosion inhibitors. Compared to conventional me...
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| Language: | English |
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Elsevier
2024-06-01
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| Series: | Materials Today Quantum |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2950257824000076 |
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| author | Muhamad Akrom Supriadi Rustad Hermawan Kresno Dipojono |
| author_facet | Muhamad Akrom Supriadi Rustad Hermawan Kresno Dipojono |
| author_sort | Muhamad Akrom |
| collection | DOAJ |
| description | This work used a variational quantum circuit (VQC) in conjunction with a quantitative structure-property relationship (QSPR) model to completely investigate the corrosion inhibition efficiency (CIE) displayed by pyridine-quinoline compounds acting as corrosion inhibitors. Compared to conventional methods like multilayer perceptron neural networks (MLPNN), the VQC model predicts the CIE more accurately. With a coefficient of determination (R2), root mean square error (RMSE), mean absolute error (MAE), and mean absolute deviation (MAD) values of 0.989, 0.027, 0.024, and 0.019, respectively, VQC performs better. The established VQC model predicts the CIE with outstanding predictive accuracy for four newly synthesized pyrimidine derivative compounds: 1-(4-fluorophenyl)- 3-(4-(pyridin-4-ylmethyl)phenyl)urea (P1), 1-phenyl-3-(4-(pyridin-4-ylmethyl)phenyl)urea (P2), 1-(4-methylphenyl)-3-(4-(pyridin-4-ylmethyl)phenyl)urea (P3), and quaternary ammonium salt dimer (P4). It generates remarkably high CIE values of 92.87, 94.05, 94.96, and 96.93 for P1, P2, P3, and P4, respectively. With its ability to streamline the testing and production processes for novel anti-corrosion materials, this innovative approach holds the potential to revolutionize the market. |
| format | Article |
| id | doaj-art-b5a685a095a1493a8ff433e4732cbeae |
| institution | DOAJ |
| issn | 2950-2578 |
| language | English |
| publishDate | 2024-06-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Materials Today Quantum |
| spelling | doaj-art-b5a685a095a1493a8ff433e4732cbeae2025-08-20T02:51:39ZengElsevierMaterials Today Quantum2950-25782024-06-01210000710.1016/j.mtquan.2024.100007Variational quantum circuit-based quantum machine learning approach for predicting corrosion inhibition efficiency of pyridine-quinoline compoundsMuhamad Akrom0Supriadi Rustad1Hermawan Kresno Dipojono2Study Program in Informatics Engineering, Faculty of Computer Science, Universitas Dian Nuswantoro, Semarang 50131, Indonesia; Research Center for Materials Informatics, Faculty of Computer Science, Universitas Dian Nuswantoro, Semarang 50131, Indonesia; Corresponding author at: Research Center for Materials Informatics, Faculty of Computer Science, Universitas Dian Nuswantoro, Semarang 50131, Indonesia.Research Center for Materials Informatics, Faculty of Computer Science, Universitas Dian Nuswantoro, Semarang 50131, Indonesia; Corresponding authors.Quantum and Nano Technologies Research Group, Faculty of Industrial Technology, Institut Teknologi Bandung, Bandung 40132, Indonesia; Corresponding authors.This work used a variational quantum circuit (VQC) in conjunction with a quantitative structure-property relationship (QSPR) model to completely investigate the corrosion inhibition efficiency (CIE) displayed by pyridine-quinoline compounds acting as corrosion inhibitors. Compared to conventional methods like multilayer perceptron neural networks (MLPNN), the VQC model predicts the CIE more accurately. With a coefficient of determination (R2), root mean square error (RMSE), mean absolute error (MAE), and mean absolute deviation (MAD) values of 0.989, 0.027, 0.024, and 0.019, respectively, VQC performs better. The established VQC model predicts the CIE with outstanding predictive accuracy for four newly synthesized pyrimidine derivative compounds: 1-(4-fluorophenyl)- 3-(4-(pyridin-4-ylmethyl)phenyl)urea (P1), 1-phenyl-3-(4-(pyridin-4-ylmethyl)phenyl)urea (P2), 1-(4-methylphenyl)-3-(4-(pyridin-4-ylmethyl)phenyl)urea (P3), and quaternary ammonium salt dimer (P4). It generates remarkably high CIE values of 92.87, 94.05, 94.96, and 96.93 for P1, P2, P3, and P4, respectively. With its ability to streamline the testing and production processes for novel anti-corrosion materials, this innovative approach holds the potential to revolutionize the market.http://www.sciencedirect.com/science/article/pii/S2950257824000076Variational quantum circuitQSPRCorrosion inhibitionPyridine-quinoline |
| spellingShingle | Muhamad Akrom Supriadi Rustad Hermawan Kresno Dipojono Variational quantum circuit-based quantum machine learning approach for predicting corrosion inhibition efficiency of pyridine-quinoline compounds Materials Today Quantum Variational quantum circuit QSPR Corrosion inhibition Pyridine-quinoline |
| title | Variational quantum circuit-based quantum machine learning approach for predicting corrosion inhibition efficiency of pyridine-quinoline compounds |
| title_full | Variational quantum circuit-based quantum machine learning approach for predicting corrosion inhibition efficiency of pyridine-quinoline compounds |
| title_fullStr | Variational quantum circuit-based quantum machine learning approach for predicting corrosion inhibition efficiency of pyridine-quinoline compounds |
| title_full_unstemmed | Variational quantum circuit-based quantum machine learning approach for predicting corrosion inhibition efficiency of pyridine-quinoline compounds |
| title_short | Variational quantum circuit-based quantum machine learning approach for predicting corrosion inhibition efficiency of pyridine-quinoline compounds |
| title_sort | variational quantum circuit based quantum machine learning approach for predicting corrosion inhibition efficiency of pyridine quinoline compounds |
| topic | Variational quantum circuit QSPR Corrosion inhibition Pyridine-quinoline |
| url | http://www.sciencedirect.com/science/article/pii/S2950257824000076 |
| work_keys_str_mv | AT muhamadakrom variationalquantumcircuitbasedquantummachinelearningapproachforpredictingcorrosioninhibitionefficiencyofpyridinequinolinecompounds AT supriadirustad variationalquantumcircuitbasedquantummachinelearningapproachforpredictingcorrosioninhibitionefficiencyofpyridinequinolinecompounds AT hermawankresnodipojono variationalquantumcircuitbasedquantummachinelearningapproachforpredictingcorrosioninhibitionefficiencyofpyridinequinolinecompounds |