Optimizing Quantum Neural Networks for Predicting the Effectiveness of Drug Compounds as Corrosion Inhibitors - Mawaddah, Lubna; Rosyid, Muhammad Reesa; Santosa, Akbar Priyo; Akrom, Muhamad (Forum Kerjasama Pendidikan Tinggi, 2024)
Corrosion, caused by electrochemical reactions in corrosive environments, can degrade the quality and lifespan of materials, potentially leading to significant losses in various industrial sectors. One common strategy to reduce corrosion rates is by using corrosion inhibitors. A significant challenge in this field is the time-consuming and costly process of testing new corrosion inhibitors in the laboratory. Consequently, there is a need for more efficient and cost-effective methods to predict the effectiveness of potential corrosion inhibitors using machine learning techniques. This research addresses this problem by applying a quantum machine learning (QML) approach with quantum neural network (QNN) algorithms to evaluate the effectiveness of drug compounds as corrosion inhibitors. The study aims to optimize QNN models by investigating three different quantum circuit configurations to identify the most effective design. The results showed that Model-01, consisting of three layers, demonstrated the best performance with an MSE of 38.81, an RMSE of 6.23, and an MAE of 6.19, along with the shortest training time of 32 seconds, indicating an optimal balance between complexity and generalizability. Overall, this QML approach provides new insights into the predictive ability of QNN models in assessing the effectiveness of drug compounds as corrosion inhibitors, demonstrating the potential of quantum computing to enhance predictive accuracy and efficiency in investigating anti-corrosion materials