This paper presents a quantitative structure–property relationship (QSPR)-based machine learning (ML) framework designed for predicting corrosion inhibition efficiency (CIE) values in natural organic inhibitor compounds. The modeling dataset comprises 50 natural organic compounds, with 11 quantum chemical properties (QCP) serving as input features, and the target variable being the corrosion inhibition efficiency (CIE) value. To enhance the predictive accuracy of the ML model, the kernel density estimation (KDE) function is employed to generate virtual samples during the training process, with the overarching goal of refining the precision of the ML model. Three distinct models, namely random forest (RF), gradient boosting (GB), and k-nearest neighbor (KNN), are tested in the study. The results demonstrate a noteworthy enhancement in the prediction performance of the models, attributable to the incorporation of virtual samples that effectively improve the correlation between input features and target values. Consequently, the accuracy of the predicted CIE values is significantly augmented, aligning more closely with the actual CIE values. Performance improvements were evident across all models after the incorporation of virtual samples. The GB, RF, and KNN models exhibited increments in R² values from 0.557 to 0.996, 0.522 to 0.999, and 0.415 to 0.994, respectively, concomitant with the introduction of 500 virtual samples. Additionally, each model demonstrated a notable reduction in RMSE values, transitioning from 1.41 to 0.19, 1.27 to 0.10, and 1.22 to 0.16, respectively. While the GB model initially outperformed others before the addition of virtual samples, the performance of the model exhibited fluctuation as the number of virtual samples varied. This behavior suggests that the KDE function provides a certain level of resilience against model variations. The proposed approach contributes to the effective design and exploration of corrosion inhibitor candidates, offering a reliable and accurate predictive tool that bridges the gap between theoretical studies and experimental synthesis.

Quantum computing has emerged as a promising technology capable of solving complex computational problems more efficiently than classical computers. Among the various quantum algorithms developed, the Quantum Support Vector Machine (QSVM) has gained significant attention for its potential to enhance machine learning tasks, particularly classification. This review paper explores the theoretical foundations, methodologies, and potential advantages of QSVM for classification tasks. We discuss the quantum computing principles underpinning QSVM, compare them with classical support vector machines, and review recent advancements and applications. Finally, we highlight the challenges and prospects of QSVM in the context of quantum machine learning.