Quantum-Inspired Algorithms for NP-Hard Problems: A Novel Heuristic Approach to Combinatorial Optimization
Abstract
NP-hard combinatorial optimization problems pose significant challenges due to their computational complexity and the exponential growth of the solution space. Quantum-inspired algorithms (QIAs), which leverage principles from quantum computing within classical frameworks, have emerged as promising heuristics for tackling these problems. This paper presents a novel heuristic approach that integrates quantum-inspired techniques with classical optimization methods to address NP-hard problems effectively. The proposed algorithm demonstrates improved solution quality and computational efficiency on benchmark combinatorial problems, highlighting the potential of quantum-inspired methodologies in classical computing environments