Utilization of Quantum Computing for Solving Large-Scale Computational Problems

Abstract

The rapid growth of data volume and problem complexity in modern computing environments has increased the demand for computational approaches capable of handling large-scale problems efficiently. Quantum computing has emerged as a promising paradigm that leverages the principles of quantum mechanics to overcome the limitations of classical computing. This article aims to examine the utilization of quantum computing in solving large-scale computational problems. The research method employed is a literature review, analyzing scientific publications, reference books, and recent research studies related to quantum computing and large-scale computation. The findings indicate that quantum computing offers significant advantages in addressing large-scale problems, particularly in optimization, simulation, machine learning, and data-intensive applications. Quantum algorithms have demonstrated the potential to reduce computational complexity and processing time for problems that are intractable for classical systems. However, practical utilization remains constrained by challenges such as hardware scalability, error rates, algorithmic complexity, and integration with classical computing infrastructures. Therefore, continued research and development are required to fully harness quantum computing for large-scale computational problem solving in future computing systems.