Exploring Quantum-Inspired Algorithms for High-Performance Computing in Structural Analysis
DOI:
https://doi.org/10.22399/ijcesen.1003Keywords:
Quantum-Inspired Algorithms, High-Performance Computing, Structural Analysis, Finite Element Analysis, Variational Monte Carlo, Quantum TunnelingAbstract
Structural analysis in high-performance computing (HPC) faces challenges related to computational complexity, energy efficiency, and solution accuracy. This research explores Quantum-Inspired Algorithms (QIAs) as an innovative approach to enhance computational efficiency and accuracy in large-scale structural simulations. The proposed methodology integrates a Quantum-Inspired Evolutionary Algorithm (QIEA) with a Hybrid Quantum-Inspired Neural Network (HQINN) for improved structural performance prediction. The study evaluates QIAs on three benchmark structural problems: Bridge Load Distribution Analysis – Achieves a computational speed-up of 45% compared to classical solvers while maintaining an error rate of <0.5%. The Quantum-Inspired Variational Monte Carlo (QIVMC) method is applied to solve complex eigenvalue problems, achieving an 8× acceleration in solving large-scale stiffness matrices compared to traditional iterative solvers. Experimental validation on a high-performance computing cluster using 1,024 cores demonstrates a 55% improvement in processing speed and a 37% reduction in energy consumption. Results confirm that Quantum-Inspired Algorithms significantly outperform traditional numerical methods in structural analysis, paving the way for their adoption in next-generation engineering simulations. Future work will focus on hybrid quantum-classical frameworks and their real-world applications in civil, aerospace, and automotive engineering.
References
Wang, Y., Li, X., & Zhang, H. (2023). High-performance structural analysis using quantum-inspired algorithms. Journal of Computational Engineering, 45(2), 123-140.
Kumar, R., & Sharma, P. (2022). A review of quantum-inspired optimization techniques for structural engineering. Advances in Structural Mechanics, 38(4), 567-589.
Zhang, L., Chen, Y., & Sun, W. (2021). Enhancing seismic response modeling with quantum evolutionary algorithms. Structural and Earthquake Engineering, 29(1), 45-62.
Jones, M., & Patel, D. (2020). Quantum-inspired finite element methods for large-scale simulations. International Journal of Computational Methods in Engineering Science, 12(3), 198-217.
Lee, J., & Wang, C. (2023). Hybrid quantum-classical approaches in high-performance computing. Engineering Computations, 41(6), 897-923.
Singh, A., & Gupta, R. (2021). Application of quantum-inspired swarm intelligence in material optimization. Journal of Structural Optimization, 33(5), 287-304.
Nguyen, T., & Park, J. (2022). Efficient eigenvalue computations using quantum variational Monte Carlo methods. Journal of Computational Mechanics, 39(2), 152-170.
Kim, S., & Tanaka, H. (2023). Quantum-enhanced finite element analysis for aerospace structures. Aerospace Structural Engineering Review, 27(4), 341-358.
Brown, K., & Anderson, T. (2020). Quantum-inspired artificial intelligence in structural health monitoring. Structural Health Monitoring Journal, 18(7), 615-632.
Zhao, F., & Lin, Y. (2021). Large-scale stiffness matrix solutions using quantum-inspired tensor networks. Journal of Applied Mathematics and Computation, 47(3), 221-238.
Patel, R., & Li, F. (2022). A comparative study of classical and quantum-inspired optimization methods in engineering. Optimization and Engineering, 35(1), 101-120.
Jackson, H., & Thompson, B. (2023). Quantum-inspired machine learning for structural failure prediction. Engineering AI and Applications, 14(2), 89-105.
Wong, P., & Hu, S. (2021). Quantum-assisted topology optimization in structural mechanics. Advances in Mechanical Engineering, 22(6), 276-293.
Evans, L., & Wu, D. (2020). Seismic risk assessment using hybrid quantum-classical models. Structural Safety and Risk Analysis, 19(4), 197-212.
Kim, N., & Choi, Y. (2022). A study on the energy efficiency of quantum-inspired HPC for civil engineering applications. Journal of Smart Infrastructure Systems, 25(3), 111-128.
Lee, T., & Wang, Z. (2023). Neural network acceleration using quantum-inspired variational models. Machine Learning in Engineering, 17(8), 512-529.
Ahmed, M., & Hassan, R. (2021). Quantum-inspired metaheuristics for aerospace structural optimization. Aerospace Engineering Research, 32(5), 256-274.
Carter, J., & Moore, B. (2020). Quantum-inspired swarm intelligence in large-scale engineering simulations. Computational Intelligence in Structural Engineering, 29(6), 321-340.
Yu, H., & Sun, L. (2022). Hybrid quantum-classical computing for adaptive structural control. Smart Materials and Structures, 39(3), 189-207.
Tan, E., & Nakamura, Y. (2023). Future trends in quantum-inspired algorithms for engineering applications. Computational Mechanics and Simulation Research, 44(1), 67-85.
CHALLA, S., & Sundari DADHABAI. (2024). Limitations and Future Directions in Studying Organisational Justice in Academia: A Call for Longitudinal and Multivariate Approaches. International Journal of Computational and Experimental Science and Engineering, 10(4). https://doi.org/10.22399/ijcesen.531
E. Dayana Anandhi, & G. Velmurugan. (2025). The Impact of Financial Planning on Financial Well-Being with a mediating role of Tax Planning among salaried employees: An empirical study. International Journal of Computational and Experimental Science and Engineering, 11(1). https://doi.org/10.22399/ijcesen.965
Noorbhasha Junnu Babu, Vidya Kamma, R. Logesh Babu, J. William Andrews, Tatiraju.V.Rajani Kanth, & J. R. Vasanthi. (2025). Innovative Computational Intelligence Frameworks for Complex Problem Solving and Optimization. International Journal of Computational and Experimental Science and Engineering, 11(1). https://doi.org/10.22399/ijcesen.834
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2024 International Journal of Computational and Experimental Science and Engineering
This work is licensed under a Creative Commons Attribution 4.0 International License.
