A Comparative Analysis of Quantum-Inspired Algorithms for Solving NP-Complete Problems

Abstract

The evolution of computational science in addressing NP-complete problems has led to diverse algorithmic approaches. In this study, we provide a comparative analysis of quantum-inspired algorithms, particularly focusing on their efficiency and scalability in practical applications. Utilizing a combination of simulation and real-world data, we demonstrate that algorithms inspired by quantum principles outperform classical methods under specific conditions. Our empirical analysis, which includes quantitative metrics such as time complexity and solution accuracy, reveals a significant improvement in problem-solving efficiency. This research fills a critical gap in the literature by directly comparing these advanced methods against traditional techniques, offering insights for future advancements in computational science.