Leveraging Quantum Computing for Complex Engineering Simulations and Decision Making
DOI:
https://doi.org/10.52783/jns.v14.3156Keywords:
Quantum Computing, Engineering Simulation, Decision-Making, Quantum Algorithms, Hybrid ModelsAbstract
In this work, we study the application of quantum computing to complex engineering simulations and decisions. Using these quantum algorithms like Quantum Approximate Optimization Algorithm (QAOA), Variational Quantum Eigensolver (VQE), Quantum Support Vector Machine (QSVM) and Grover’s Algorithm, we will optimize computational efficiency, accuracy and diversity of solutions in engineering. It was performed with a comparative analysis with classical algorithms based on performance metrics such as accuracy of solution, execution time, and optimality of the decision. Analysis of experimental results showed QAOA produces a 92.3% solution accuracy, higher than that (88.6%) of classical simulated annealing and (85.4%) genetic algorithms. Similarly, VQE achieved 27% reduction in execution time versus the classical equivalent and QSVM achieved 94.1% classification accuracy on engineering pattern recognition tasks. Grover’s Algorithm also resulted in a 3.7x faster search efficiency in decision oriented simulation. Finally, the study shows that quantum inspired models can perform efficiently on large scale and highly complex engineering problems. These findings provide a major leap in realizing the capability to harness quantum technologies for real time, scalable decision making in structural analysis, lifecycle management and intelligent system design. Such results make way for future developments of hybrid quantum classical computing frameworks that could be deployed in industry.
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