This study presents a Quantum Machine Learning (QML) architecture for perfectly classifying the Iris flower dataset. The research addresses improving classification accuracy using quantum models in machine-learning tasks. The objective is to demonstrate the effectiveness of QML approaches, specifically the Variational Quantum Circuit (VQC), Quantum Neural Network (QNN), and Quantum Support Vector Machine (QSVM), in achieving high performance on the Iris dataset. The proposed methods result in perfect classification, with all models attaining accuracy, precision, recall, and an F1-score of 1.00. The main finding is that the QML architecture successfully achieves flawless classification, contributing significantly to the field. These results underscore the potential of QML in solving complex classification problems and highlight its promise for future applications across various domains. The study concludes that QML techniques can offer transformative solutions in machine learning tasks, particularly those leveraging VQC, QNN, and QSVM.

This study explores the development and evaluation of a Variational Quantum Algorithm (VQA) for predicting a drug as a corrosion inhibitor, highlighting its advantages over traditional regression models. The VQA leverages quantum-enhanced feature mapping and optimization techniques to capture complex, non-linear relationships within the data. Comparative analysis with AutoRegressive with exogenous inputs (ARX) and Gradient Boosting (GB) models demonstrate the superior performance of VQA across key metrics, including Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Deviation (MAD). The VQA achieved the lowest RMSE (4.40), MAE (3.33), and MAD (3.17) values, indicating enhanced predictive accuracy and stability. These results underscore the potential of quantum machine learning techniques in advancing predictive modeling capabilities, offering significant improvements in accuracy and consistency over classical methods. The findings suggest that VQA is a promising approach for applications requiring high precision and reliability, paving the way for broader adoption of quantum-enhanced models in material science and beyond.

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.

In this study, we compare the performance of the XGBoost model with a Support Vector Machine (SVM) model from the literature in predicting a given task. Performance metrics such as the coefficient of determination (R2), root mean squared error (RMSE), and mean absolute error (MAE) were utilized to evaluate and compare the models. The XGBoost model achieved an R² of 0.99, an RMSE of 2.54, and an MAE of 1.96, significantly outperforming the SVM model, which recorded an R² of 0.96 and an RMSE of 6.79. The scatter plot for the XGBoost model further illustrated its superior performance, showing a tight clustering of points around the ideal line (y = x), indicating high accuracy and low prediction errors. These findings suggest that the XGBoost model is highly effective for the given prediction task, likely due to its ability to capture complex patterns and interactions within the data.

In this study, we evaluate the performance of various machine learning models, including Random Forest (RF), Bagging (BAG), AdaBoost (ADA), Artificial Neural Network (ANN), and Support Vector Machine (SVM), using metrics such as R², Root Mean Squared Error (RMSE), and Mean Absolute Error (MAE). The results indicate that AdaBoost (ADA) achieves the highest performance with an R² of 0.999, RMSE of 2.32, and MAE of 2.24, making it the most accurate model with the smallest prediction errors. Bagging (BAG) also performs exceptionally well, with an R2 of 0.996, RMSE of 3.09, and MAE of 2.92. The Artificial Neural Network (ANN) exhibits a high R2 of 0.999, though RMSE and MAE values are not provided. Random Forest (RF) and Support Vector Machine (SVM) show good performance with R² values of 0.982 and 0.970, respectively, but are outperformed by the ensemble methods. The findings underscore the superiority of ensemble techniques, particularly AdaBoost, in achieving high predictive accuracy and minimal errors in this context.

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.

Empirical studies of possible compound corrosion inhibitors require a lot of money, time, and resources. Therefore, we used a machine learning (ML) paradigm based on quantitative structure-property relationship (QSPR) models to evaluate ensemble algorithms as predictors of corrosion inhibition efficiency (CIE) values. Our investigation reveals that the gradient boosting (GB) regressor model outperforms other ensemble-based models. This advantage is evaluated objectively using the metrics root mean square error (RMSE), mean absolute error (MAE), and coefficient of determination (R2). In summary, our research provides a new perspective on how well machine learning algorithms in particular ensembles work to identify organic molecules such as pyridazine that have the potential to prevent corrosion on the surfaces of metals such as iron and its alloys.

This comprehensive review delves into the realm of green corrosion inhibitors for iron alloys, focusing on a thorough exploration guided by data-driven investigation, density functional theory (DFT) simulations, and experimental validation. Harnessing the potential of plant extracts, this study scrutinizes their effectiveness in mitigating corrosion in iron alloys through a multi-faceted approach. By integrating computational modeling with empirical experimentation, a deeper understanding of the inhibitive mechanisms is achieved, offering insights into their practical application. The review synthesizes findings from diverse studies, elucidating the pivotal role of DFT in predicting inhibitor behavior and optimizing their performance. Furthermore, experimental validation provides crucial validation of theoretical predictions, highlighting the synergistic relationship between simulation and real-world application. Through this journey of exploration, the review underscores the promise of green corrosion inhibitors derived from natural sources, paving the way for sustainable corrosion control practices in the realm of iron alloys.

This study investigates machine learning-based quantitative structure-property relationship (QSPR) models for predicting the thermal stability of zinc metal-organic frameworks (Zn-MOF). Utilizing a dataset comprising 151 Zn-MOF compounds with relevant molecular descriptors, ridge (R) and kernel ridge (KR) regression models were developed and evaluated. The results demonstrate that the R model outperforms the KR model in terms of prediction accuracy, with the R model exhibiting exceptional performance (R² = 0.999, RMSE = 0.0022). While achieving high accuracy, opportunities for further improvement exist through hyperparameter optimization and exploration of polynomial functions. This research underscores the potential of ML-based QSPR models in predicting the thermal stability of Zn-MOF compounds and highlights avenues for future investigation to enhance model accuracy and applicability in materials science.

Investigating potential corrosion inhibitors via empirical research is a labor- and resource-intensive process. In this work, we evaluated various linear and non-linear algorithms as predictive models for corrosion inhibition efficiency (CIE) values using a machine learning (ML) paradigm based on the quantitative structure-property relationship (QSPR) model. In the quinoxaline compound dataset, our analysis showed that the XGBoost model performed the best predictor of other ensemble-based models. The coefficient of determination (R2), mean absolute percentage error (MAPE), and root mean squared error (RMSE) metrics were used to objectively assess this superiority. To sum up, our study offers a fresh viewpoint on the effectiveness of machine learning algorithms in determining the ability of organic compounds like quinoxaline to suppress corrosion on iron surfaces.