Implementation of quantum machine learning in predicting corrosion inhibition efficiency of expired drugs

Abstract

This study explores the potential of quantum machine learning (QML) 's potential in predicting expired pharmaceutical compounds' corrosion inhibition capacity. This investigation utilized a QSPR model where features derived from density functional theory (DFT) calculations are used as input, while the corrosion inhibition efficiency (CIE) values derived from the experimental study are used as target output. The proposed QML model exhibits varying performance through evaluation metrics, particularly concerning encoding and ansatz design. Notably, the quantum support vector machine (QSVM) demonstrates superior predictive performance compared to the variational quantum circuit (VQC) and quantum neural network (QNN). Specifically, the QSVM model achieves the highest scores in evaluation metrics, with a root mean squared error (RMSE) of 4.36, mean absolute error (MAE) of 3.19, and mean absolute deviation (MAD) of 3.08. The research highlights the importance of larger datasets to improve predictability and emphasizes the potential of QML in investigating anti-corrosion materials. Despite its limitations, this study establishes a foundational framework for utilizing QML to forecast anti-corrosive qualities.