Quantum-Inspired Heuristic Optimization for Large-Scale Cloud Resource Allocation

Efficient cloud resource allocation remains a critical challenge in large-scale distributed computing environments. Traditional heuristic methods, such as Genetic Algorithms (GA) and Particle Swarm Optimization (PSO), have limitations in terms of convergence speed and optimality. Inspired by quantum computing principles, we propose a Quantum-Inspired Heuristic Optimization (QIHO) approach that enhances cloud resource allocation efficiency. By leveraging quantum superposition and interference properties, our approach improves search space exploration and convergence rates. Experimental evaluations demonstrate a 25% reduction in computational overhead and a 30% improvement in resource utilization compared to conventional heuristics.