Quantum Computing Applications in Solving Complex Optimization Problems in Engineering Systems

Abstract

The increasing complexity of modern engineering systems has created a demand for advanced computational techniques capable of solving large-scale optimization problems efficiently. Classical computing approaches, while effective for many applications, often struggle with combinatorial complexity and high-dimensional problem spaces. Quantum computing has emerged as a promising paradigm that leverages quantum mechanical principles to perform computations beyond the capabilities of classical systems. This study explores the application of quantum computing in solving complex optimization problems in engineering domains, including power systems, transportation networks, and manufacturing processes. The research focuses on quantum algorithms such as the Quantum Approximate Optimization Algorithm (QAOA) and Variational Quantum Eigensolver (VQE), evaluating their performance in comparison to classical optimization methods. Simulation results demonstrate that quantum approaches offer significant advantages in solving combinatorial optimization problems, particularly in terms of speed and solution quality. The study also addresses current limitations, including hardware constraints and error rates, while highlighting future prospects for quantum-enhanced optimization in engineering systems.