Optimization Techniques on Quantum and Classical Systems: A Comprehensive Comparative Study - Shaik Hussain; Rajitha Morampudi; Priyamvada K.; Bhargavi R. Naveena (E3S Web of Conferences)
Quantum optimization is a promising field revolutionizing problem-solving across domains. This study compares Particle Swarm Optimization (PSO), Moth Flame Optimization (MFO), and Genetic Algorithm (GA) on three platforms : a local computer, a local computer with quantum integration, and an IBM quantum machine. Results indicate PSO’s consistent performance across all setups, with the IBM quantum machine having a longer elapsed time. For MFO, the optimal solution is found using the IBM quantum machine, despite its longer execution time. Similarly, GA achieves the best results on the IBM quantum machine. These findings suggest that while quantum computers excel in solving complex problems, their execution time for simpler tasks remains higher than classical setups. Future research should address challenges like noise, limited qubits, and high material costs to improve quantum computers’ efficiency and availability.