STM Journals Peer-reviewed research from Consortium e-Learning Network

Journal of Polymer & Composites Original Research

Hybrid Quantum–Machine Learning Framework for Nonlinear Rheological Modeling of Polymer and Composite Materials

  1. Pijush Dutta Department of Computer Science & Engineering, Greater Kolkata College of Engineering and Management
  2. Pradip Deb Department of Bachelor of Computer Application, Global College of Science and Technology, Krishnagar
  3. Pooja Nagpal Faculty of Management, CMS Business School, Jain (Deemed to Be University), Bangalore
  4. Shantanu Naskar Department of Electrical Engineering, Greater Kolkata College of Engineering and Management
  5. Anubrata Mondal Department of Electrical Engineering, Greater Kolkata College of Engineering and Management
  6. Tan Yi Fei Centre for Smart Systems and Automation, COE for Robotics and Sensing Technologies, Faculty of Artificial Intelligence and Engineering, Multimedia University, Persiaran Multimedia, 63100 Cyberjaya, Selangor
  7. Chua Fang Centre for Advanced Analytics, COE for Artificial Intelligence, Faculty of Computing and Informatics, Multimedia University, Persiaran Multimedia, 63100 Cyberjaya, Selangor

Abstract

In polymer and composite materials, a major challenge lies in predicting their nonlinear rheological response, owing to complex multiscale interactions that are not captured by traditional constitutive laws or conventional machine learning approaches. In this study, a hybrid Quantum Machine Learning (QML) model comprising Quantum Support Vector Machine (QSVM) and Quantum Neural Network (QNN) architectures is proposed for viscosity prediction without requiring any specific rheological equation. To train and test the proposed model, 420 samples, including shear rate, temperature, and composition-related parameters, were employed. Statistical metrics like MSE, RMSE, MAE, and were used to evaluate the predictive performance. The results show that both quantum models outperform the classical machine learning approaches, where the QNN has the highest prediction accuracy ( =0.970) with RMSE = 0.0435 and MAE = 0.0324 for the testing set, and also has a very good generalization capacity. Moreover, a comparative study was also performed on this model for validation purposes. A sensitivity analysis indicated Polymer concentration and temperature as the key parameters determining the viscosity behavior. The results prove the potential of hybrid quantum–classical frameworks as powerful and data-rich tools for rheological modeling, the intelligent optimization of manufacturing processes, and future developments in materials informatics in Industry 5.0.

Keywords

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