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Advance Sustainable Science Engineering and Technology

Harnessing Quantum SVR on Quantum Turing Machine for Drug Compounds Corrosion Inhibitors Analysis

Akbar Priyo Santosa, Muhammad Reesa Rosyid, Lubna Mawaddah, Muhamad Akrom

Published Jul 27, 2024• 1 citation
Publisher

Abstract

Corrosion is an issue that has a significant impact on the oil and gas industry, resulting in significant losses. This is worth investigating because corrosion contributes to a large part of the total annual costs of oil and gas production companies worldwide, and can cause serious problems for the environment that will impact society. The use of inhibitors is one way to prevent corrosion that is quite effective. This study is an experimental study that aims to implement machine learning (ML) on the efficiency of corrosion inhibitors. In this study, the use of the Quantum Support Vector Regression (QSVR) algorithm in the ML approach is used considering the increasingly developing quantum computing technology with the aim of producing better evaluation matrix values ​​than the classical ML algorithm. From the experiments carried out, it was found that the QSVR algorithm with a combination of (TrainableFidelityQuantumKernel, ZZFeatureMap/ PauliFeatureMap, and linear entanglement) obtained better Root Mean Square Error (RMSE) and model training time with a value of 6,19 and 92 compared to other models in this experiment which can be considered in predicting the efficiency of corrosion inhibitors. The success of the research model can provide a new insights of the ability of quantum computer algorithms to increase the evaluation value of the matrix and the ability of ML to predict the efficiency of corrosion inhibitors, especially on a large industrial scale.

DOI
10.26877/asset.v6i3.601

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Corrosion is an issue that has a significant impact on the oil and gas industry, resulting in significant losses. This is worth investigating because corrosion contributes to a large part of the total annual costs of oil and gas production companies worldwide, and can cause serious problems for the environment that will impact society. The use of inhibitors is one way to prevent corrosion that is quite effective. This study is an experimental study that aims to implement machine learning (ML) on the efficiency of corrosion inhibitors. In this study, the use of the Quantum Support Vector Regression (QSVR) algorithm in the ML approach is used considering the increasingly developing quantum computing technology with the aim of producing better evaluation matrix values ​​than the classical ML algorithm. From the experiments carried out, it was found that the QSVR algorithm with a combination of (TrainableFidelityQuantumKernel, ZZFeatureMap/ PauliFeatureMap, and linear entanglement) obtained better Root Mean Square Error (RMSE) and model training time with a value of 6,19 and 92 compared to other models in this experiment which can be considered in predicting the efficiency of corrosion inhibitors. The success of the research model can provide a new insights of the ability of quantum computer algorithms to increase the evaluation value of the matrix and the ability of ML to predict the efficiency of corrosion inhibitors, especially on a large industrial scale.
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
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