Quantum Computing Applications in Engineering Optimization Solving Complex Multi-Variable Industrial Problems

Abstract

The Quantum computing has emerged as a disruptive paradigm with the potential to revolutionize engineering optimization by addressing computationally intractable problems. Classical optimization techniques often struggle with large-scale, multi-variable industrial problems due to exponential complexity and computational constraints. This paper explores the application of quantum computing algorithms, including quantum annealing and variational quantum algorithms, for solving complex engineering Optimisation tasks. A hybrid quantum-classical framework is proposed to integrate quantum processors with classical computational systems for enhanced performance. The study focuses on applications in supply chain optimization, energy systems, and manufacturing processes. Simulation results demonstrate that quantum-inspired algorithms significantly improve solution quality and computational efficiency compared to traditional methods. The framework leverages qubit superposition and entanglement to explore large solution spaces simultaneously, enabling faster convergence to optimal solutions. Furthermore, the integration of machine learning techniques enhances adaptability and scalability. The findings highlight the potential of quantum computing in transforming engineering optimization and industrial decision-making. This research contributes to the advancement of next-generation computational methods capable of solving complex real-world problems efficiently.