Variational Quantum Circuit-Based Quantum Machine Learning Approach for Predicting Corrosion Inhibition Efficiency of Expired Pharmaceuticals

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

doi:10.15575/join.v10i1.1333

Authors

Muhamad Akrom Research Center for Quantum Computing and Materials Informatics, Faculty of Computer Science, Universitas Dian Nuswantoro, Indonesia
Muhammad Reesa Rosyid Faculty of Computer Science, Universitas Dian Nuswantoro, Indonesia
Lubna Mawaddah Faculty of Computer Science, Universitas Dian Nuswantoro, Indonesia
Akbar Priyo Santosa Faculty of Computer Science, Universitas Dian Nuswantoro, Indonesia

DOI:

https://doi.org/10.15575/join.v10i1.1333

Keywords:

Ansatz Designs, Corrosion Inhibitors, Drug Compounds, Quantum Machine Learning, Variational Quantum Circuit

Abstract

This study examines the potential of quantum machine learning (QML) to predict the corrosion inhibition capacity of expired pharmaceutical compounds. The investigation employs a QSPR model, using features generated from density functional theory (DFT) calculations as input. At the same time, corrosion inhibition efficiency (CIE) values obtained from experimental data serve as the target output. The VQC model demonstrates varied performance across evaluation metrics, especially with encoding and ansatz design. The model achieves fine scores in evaluation metrics, with root mean square error (RMSE) of 6.15, mean absolute error (MAE) of 5.63, and mean absolute deviation (MAD) of 5.50. The research underscores the significance of larger datasets for enhancing predictive accuracy and points to QML's potential in exploring anti-corrosion materials. Although there are some limitations, this study provides a foundational framework for using QML to predict anti-corrosive properties.

References

[1] Y. Cui, T. Zhang, and F. Wang, “New understanding on the mechanism of organic inhibitors for magnesium alloy,” Corros Sci, vol. 198, p. 110118, Apr. 2022, doi: 10.1016/j.corsci.2022.110118.

[2] T. K. Sarkar, V. Saraswat, R. K. Mitra, I. B. Obot, and M. Yadav, “Mitigation of corrosion in petroleum oil well/tubing steel using pyrimidines as efficient corrosion inhibitor: Experimental and theoretical investigation,” Mater Today Commun, vol. 26, p. 101862, Mar. 2021, doi: 10.1016/j.mtcomm.2020.101862.

[3] C. Beltran-Perez et al., “A General Use QSAR-ARX Model to Predict the Corrosion Inhibition Efficiency of Drugs in Terms of Quantum Mechanical Descriptors and Experimental Comparison for Lidocaine,” Int J Mol Sci, vol. 23, no. 9, p. 5086, May 2022, doi: 10.3390/ijms23095086.

[4] D. K. Kozlica, A. Kokalj, and I. Milošev, “Synergistic effect of 2-mercaptobenzimidazole and octylphosphonic acid as corrosion inhibitors for copper and aluminium – An electrochemical, XPS, FTIR and DFT study,” Corros Sci, vol. 182, p. 109082, Apr. 2021, doi: 10.1016/j.corsci.2020.109082.

[5] M. Akrom, Investigation of natural extracts as green corrosion inhibitors in steel using density functional theory, Jurnal Teori Dan Aplikasi Fisika, 10(1), 89–102 (2022).

[6] C. Verma, E. E. Ebenso, and M. A. Quraishi, “Alkaloids as green and environmental benign corrosion inhibitors: An overview,” International Journal of Corrosion and Scale Inhibition, vol. 8, no. 3, pp. 512–528, 2019, doi: 10.17675/2305-6894-2019-8-3-3.

[7] S. Lim and S. Chi, “Xgboost application on bridge management systems for proactive damage estimation,” Advanced Engineering Informatics, vol. 41, p. 100922, Aug. 2019, doi: 10.1016/j.aei.2019.100922.

[8] M. Akrom, Green corrosion inhibitors for iron alloys: a comprehensive review of integrating data-driven forecasting, density functional theory simulations, and experimental investigation, Journal of Multiscale Materials Informatics, 1(1), 22-37 (2022), DOI: https://doi.org/10.62411/jimat.v1i1.10495.

[9] R. I. D. Putra, A. L. Maulana, and A. G. Saputro, “Study on building machine learning model to predict biodegradable-ready materials,” 2019, p. 060003. doi: 10.1063/1.5095351.

[10] D. Kumar, V. Jain, and B. Rai, “Capturing the synergistic effects between corrosion inhibitor molecules using density functional theory and ReaxFF simulations - A case for benzyl azide and butyn-1-ol on Cu surface,” Corros Sci, vol. 195, p. 109960, Feb. 2022, doi: 10.1016/j.corsci.2021.109960.

[11] T. W. Quadri et al., “Multilayer perceptron neural network-based QSAR models for the assessment and prediction of corrosion inhibition performances of ionic liquids,” Comput Mater Sci, vol. 214, p. 111753, Nov. 2022, doi: 10.1016/j.commatsci.2022.111753.

[12] A. H. Alamri and N. Alhazmi, “Development of data driven machine learning models for the prediction and design of pyrimidine corrosion inhibitors,” Journal of Saudi Chemical Society, vol. 26, no. 6, p. 101536, Nov. 2022, doi: 10.1016/j.jscs.2022.101536.

[13] M. E. A. Ben Seghier, D. Höche, and M. Zheludkevich, “Prediction of the internal corrosion rate for oil and gas pipeline: Implementation of ensemble learning techniques,” J Nat Gas Sci Eng, vol. 99, p. 104425, Mar. 2022, doi: 10.1016/j.jngse.2022.104425.

[14] M. Akrom, S. Rustad, A. G. Saputro, and H. K. Dipojono, “Data-driven investigation to model the corrosion inhibition efficiency of Pyrimidine-Pyrazole hybrid corrosion inhibitors,” Comput Theor Chem, vol. 1229, p. 114307, Nov. 2023, doi: 10.1016/j.comptc.2023.114307.

[15] M. R. Rosyid, L. Mawaddah, A. P. Santosa, M. Akrom, S. Rustad, and H. K. Dipojono, “Implementation of quantum machine learning in predicting corrosion inhibition efficiency of expired drugs,” Mater Today Commun, vol. 40, Aug. 2024, doi: 10.1016/j.mtcomm.2024.109830.

[16] T. W. Quadri et al., “Development of QSAR-based (MLR/ANN) predictive models for effective design of pyridazine corrosion inhibitors,” Mater Today Commun, vol. 30, p. 103163, Mar. 2022, doi: 10.1016/j.mtcomm.2022.103163.

[17] M. Akrom, S. Rustad, A. G. Saputro, A. Ramelan, F. Fathurrahman, and H. K. Dipojono, “A combination of machine learning model and density functional theory method to predict corrosion inhibition performance of new diazine derivative compounds,” Mater Today Commun, vol. 35, p. 106402, Jun. 2023, doi: 10.1016/j.mtcomm.2023.106402.

[18] A. H. Alamri and N. Alhazmi, “Development of data driven machine learning models for the prediction and design of pyrimidine corrosion inhibitors,” Journal of Saudi Chemical Society, vol. 26, no. 6, p. 101536, Nov. 2022, doi: 10.1016/j.jscs.2022.101536.

[19] T. H. Pham, P. K. Le, and D. N. Son, “A data-driven QSPR model for screening organic corrosion inhibitors for carbon steel using machine learning techniques,” RSC Adv, vol. 14, no. 16, pp. 11157–11168, 2024, doi: 10.1039/D4RA02159B.

[20] S. Butmaratthaya, N. Buesamae, and U. Taetragool, “MNIST quantum classification models implementation and benchmarking,” 2023, p. 070002. doi: 10.1063/5.0178776.

[21] N. Buesamae and U. Taetragool, “Tuning variational quantum classifier with automated design,” 2023, p. 070003. doi: 10.1063/5.0179294.

[22] J. Alcazar, V. Leyton-Ortega, and A. Perdomo-Ortiz, “Classical versus quantum models in machine learning: insights from a finance application,” Mach Learn Sci Technol, vol. 1, no. 3, p. 035003, Jul. 2020, doi: 10.1088/2632-2153/ab9009.

[23] M. Schuld, A. Bocharov, K. M. Svore, and N. Wiebe, “Circuit-centric quantum classifiers,” Phys Rev A (Coll Park), vol. 101, no. 3, p. 032308, Mar. 2020, doi: 10.1103/PhysRevA.101.032308.

[24] M. Akrom, S. Rustad, and H. K. Dipojono, “Development of quantum machine learning to evaluate the corrosion inhibition capability of pyrimidine compounds,” Mater Today Commun, vol. 39, p. 108758, Jun. 2024, doi: 10.1016/j.mtcomm.2024.108758.

[25] M. Akrom, S. Rustad, and H. K. Dipojono, “Variational quantum circuit-based quantum machine learning approach for predicting corrosion inhibition efficiency of pyridine-quinoline compounds,” Materials Today Quantum, vol. 2, p. 100007, Jun. 2024, doi: 10.1016/j.mtquan.2024.100007.

[26] L. B. V. de Amorim, G. D. C. Cavalcanti, and R. M. O. Cruz, “The choice of scaling technique matters for classification performance,” Appl Soft Comput, vol. 133, p. 109924, Jan. 2023, doi: 10.1016/j.asoc.2022.109924.

[27] I. Lukovits, E. Kálmán, and F. Zucchi, “Corrosion Inhibitors—Correlation between Electronic Structure and Efficiency,” CORROSION, vol. 57, no. 1, pp. 3–8, Jan. 2001, doi: 10.5006/1.3290328.

[28] Y. Liu et al., “A Machine Learning-Based QSAR Model for Benzimidazole Derivatives as Corrosion Inhibitors by Incorporating Comprehensive Feature Selection,” Interdiscip Sci, vol. 11, no. 4, pp. 738–747, Dec. 2019, doi: 10.1007/s12539-019-00346-7.

[29] G. Ibarra-Vazquez, M. S. Ramírez-Montoya, and J. Miranda, “Data Analysis in Factors of Social Entrepreneurship Tools in Complex Thinking: An exploratory study,” Think Skills Creat, vol. 49, p. 101381, Sep. 2023, doi: 10.1016/j.tsc.2023.101381.

[30] J. Linden and R. Marquis, “The influence of time on dynamic signature: An exploratory data analysis,” Forensic Sci Int, vol. 348, p. 111577, Jul. 2023, doi: 10.1016/j.forsciint.2023.111577.

[31] M. Akrom, T. Sutojo, A. Pertiwi, S. Rustad, and H. Kresno Dipojono, “Investigation of Best QSPR-Based Machine Learning Model to Predict Corrosion Inhibition Performance of Pyridine-Quinoline Compounds,” J Phys Conf Ser, vol. 2673, no. 1, p. 012014, Dec. 2023, doi: 10.1088/1742-6596/2673/1/012014.

[32] M. Ahsan, M. Mahmud, P. Saha, K. Gupta, and Z. Siddique, “Effect of Data Scaling Methods on Machine Learning Algorithms and Model Performance,” Technologies (Basel), vol. 9, no. 3, p. 52, Jul. 2021, doi: 10.3390/technologies9030052.

[33] M. Akrom, S. Rustad, A. G. Saputro, A. Ramelan, F. Fathurrahman, and H. K. Dipojono, “A combination of machine learning model and density functional theory method to predict corrosion inhibition performance of new diazine derivative compounds,” Mater Today Commun, vol. 35, p. 106402, Jun. 2023, doi: 10.1016/j.mtcomm.2023.106402.

[34] T. Kumar, D. Kumar, and G. Singh, “Brain Tumour Classification Using Quantum Support Vector Machine Learning Algorithm,” IETE J Res, vol. 70, no. 5, pp. 4815–4828, May 2024, doi: 10.1080/03772063.2023.2245350.

[35] A. Pyrkov et al., “Quantum computing for near-term applications in generative chemistry and drug discovery,” Drug Discov Today, vol. 28, no. 8, p. 103675, Aug. 2023, doi: 10.1016/j.drudis.2023.103675.

[36] Z. Deng, X. Wang, and B. Dong, “Quantum computing for future real-time building HVAC controls,” Appl Energy, vol. 334, p. 120621, Mar. 2023, doi: 10.1016/j.apenergy.2022.120621.

[37] B. A. Alhayani, O. A. AlKawak, H. B. Mahajan, H. Ilhan, and R. M. Qasem, “Design of Quantum Communication Protocols in Quantum Cryptography,” Wirel Pers Commun, Jul. 2023, doi: 10.1007/s11277-023-10587-x.

[38] P. Brown and H. Zhuang, “Quantum machine-learning phase prediction of high-entropy alloys,” Materials Today, vol. 63, pp. 18–31, Mar. 2023, doi: 10.1016/j.mattod.2023.02.014.

[39] J. Biamonte, P. Wittek, N. Pancotti, P. Rebentrost, N. Wiebe, and S. Lloyd, “Quantum machine learning,” Nature, vol. 549, no. 7671, pp. 195–202, Sep. 2017, doi: 10.1038/nature23474.

[40] H. Gupta, H. Varshney, T. K. Sharma, N. Pachauri, and O. P. Verma, “Comparative performance analysis of quantum machine learning with deep learning for diabetes prediction,” Complex & Intelligent Systems, vol. 8, no. 4, pp. 3073–3087, Aug. 2022, doi: 10.1007/s40747-021-00398-7.

[41] R. Xia and S. Kais, “Hybrid Quantum-Classical Neural Network for Calculating Ground State Energies of Molecules,” Entropy, vol. 22, no. 8, p. 828, Jul. 2020, doi: 10.3390/e22080828.

[42] Y. Kwak, W. J. Yun, S. Jung, and J. Kim, “Quantum Neural Networks: Concepts, Applications, and Challenges,” Aug. 2021, [Online]. Available: http://arxiv.org/abs/2108.01468

[43] S. Aishwarya, V. Abeer, B. B. Sathish, and K. N. Subramanya, “Quantum Computational Techniques for Prediction of Cognitive State of Human Mind from EEG Signals,” Journal of Quantum Computing, vol. 2, no. 4, pp. 157–170, 2020, doi: 10.32604/jqc.2020.015018.

[44] A. Sagingalieva, M. Kordzanganeh, N. Kenbayev, D. Kosichkina, T. Tomashuk, and A. Melnikov, “Hybrid Quantum Neural Network for Drug Response Prediction,” Cancers (Basel), vol. 15, no. 10, p. 2705, May 2023, doi: 10.3390/cancers15102705.

[45] N. Mishra et al., “Quantum Machine Learning: A Review and Current Status,” 2021, pp. 101–145. doi: 10.1007/978-981-15-5619-7_8.

[46] Z. Ozpolat and M. Karabatak, “Performance Evaluation of Quantum-Based Machine Learning Algorithms for Cardiac Arrhythmia Classification,” Diagnostics, vol. 13, no. 6, p. 1099, Mar. 2023, doi: 10.3390/diagnostics13061099.

[47] M. J. Kholili, R. Muslim, and A. R. T. Nugraha, “A classical algorithm inspired by quantum neural network for solving a Bose-Hubbard-like system in phase-space representation,” 2023, p. 070007. doi: 10.1063/5.0178381.

[48] T. Imanothai and U. Taetragool, “The effects of training quantum support vector machines with different samples from the same dataset,” 2023, p. 070006. doi: 10.1063/5.0178310.

[49] G. Abdulsalam, S. Meshoul, and H. Shaiba, “Explainable Heart Disease Prediction Using Ensemble-Quantum Machine Learning Approach,” Intelligent Automation & Soft Computing, vol. 36, no. 1, pp. 761–779, 2023, doi: 10.32604/iasc.2023.032262.

[50] E. I. Elsedimy, S. M. M. AboHashish, and F. Algarni, “New cardiovascular disease prediction approach using support vector machine and quantum-behaved particle swarm optimization,” Multimed Tools Appl, vol. 83, no. 8, pp. 23901–23928, Aug. 2023, doi: 10.1007/s11042-023-16194-z.

[51] J. Qin, “Review of ansatz designing techniques for variational quantum algorithms,” J Phys Conf Ser, vol. 2634, no. 1, p. 012043, Nov. 2023, doi: 10.1088/1742-6596/2634/1/012043.

[52] W. Aboumrad and D. Widdows, “Mod2VQLS: A Variational Quantum Algorithm for Solving Systems of Linear Equations Modulo 2,” Applied Sciences, vol. 14, no. 2, p. 792, Jan. 2024, doi: 10.3390/app14020792.

[53] N. Nguyen and K.-C. Chen, “Quantum Embedding Search for Quantum Machine Learning,” IEEE Access, vol. 10, pp. 41444–41456, 2022, doi: 10.1109/ACCESS.2022.3167398.

[54] J. Biamonte, P. Wittek, N. Pancotti, P. Rebentrost, N. Wiebe, and S. Lloyd, “Quantum machine learning,” Nature, vol. 549, no. 7671, pp. 195–202, Sep. 2017, doi: 10.1038/nature23474.

[55] M. Wieder, J. Fass, and J. D. Chodera, “Fitting quantum machine learning potentials to experimental free energy data: predicting tautomer ratios in solution,” Chem Sci, vol. 12, no. 34, pp. 11364–11381, 2021, doi: 10.1039/D1SC01185E.

[56] L. Yang and A. Shami, “On hyperparameter optimization of machine learning algorithms: Theory and practice,” Neurocomputing, vol. 415, pp. 295–316, Nov. 2020, doi: 10.1016/j.neucom.2020.07.061.

[57] Z. M. Alhakeem, Y. M. Jebur, S. N. Henedy, H. Imran, L. F. A. Bernardo, and H. M. Hussein, “Prediction of Ecofriendly Concrete Compressive Strength Using Gradient Boosting Regression Tree Combined with GridSearchCV Hyperparameter-Optimization Techniques,” Materials, vol. 15, no. 21, p. 7432, Oct. 2022, doi: 10.3390/ma15217432.

[58] W. Herowati et al., “Prediction of Corrosion Inhibition Efficiency Based on Machine Learning for Pyrimidine Compounds: A Comparative Study of Linear and Non-linear Algorithms,” KnE Engineering, Mar. 2024, doi: 10.18502/keg.v6i1.15350.

[59] S. Budi et al., “Implementation of Polynomial Functions to Improve the Accuracy of Machine Learning Models in Predicting the Corrosion Inhibition Efficiency of Pyridine-Quinoline Compounds as Corrosion Inhibitors,” KnE Engineering, Mar. 2024, doi: 10.18502/keg.v6i1.15351.