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All Journal Journal of Multiscale Materials Informatics Advance Sustainable Science, Engineering and Technology (ASSET)
Akbar Priyo Santosa
Universitas Dian Nuswantoro
Chemical Engineering, Chemistry & Bioengineering Chemistry Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Industrial & Manufacturing Engineering Materials Science & Nanotechnology

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Journal : journal of multiscale materials informatics

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Quantum support vector regression for predicting corrosion inhibition of drugs Akbar Priyo Santosa; Muhamad Akrom
Journal of Multiscale Materials Informatics Vol. 1 No. 2 (2024): October
Publisher : Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/jimat.v1i2.11427

Abstract

This study evaluates the performance of Quantum Support Vector Regression (QSVR) in predicting material properties using limited data. Experimental results show that the QSVR model consistently produces superior prediction accuracy compared to previous conventional regression models. This improvement is especially evident in the prediction accuracy for small and complex datasets, where QSVR can better capture non-linear patterns. The superiority of QSVR in processing data with a quantum approach provides great potential in developing predictive models in materials science and computational chemistry.
Co-Authors Lubna Mawaddah Muhamad Akrom Muhammad Reesa