Quantum Optimization Techniques and It’s Comparison with Classical Optimization

Abstract

Quantum computers are best known for its outstanding performance. Harnessing the last 10 years of advancement of technology in hardware and software, the computational complexity a measure of time needed to execute complex optimization problems plays a significant role in performance of the system. Some of the classical optimization algorithms use random seeds to converge and sometimes bad random seeds lead to non-convergence and utilize lots of computational resources to solve complex optimization problems. As quantum computing comes with supremacy of computing, in this paper an attempt has been made to explicate and review quantum optimization algorithms such as Genetic Quantum Algorithm (GQA) and Quantum Approximate Optimization algorithm (QAOA) and its comparison with classical optimization algorithms.