Exploring Optimization Techniques For Large-scale Problems: A Study Of Simulated Annealing And Quantum Annealing On The Ising Model And Max-cut Problem With 2000 Nodes

Abstract

Simulated Annealing (SA) is a heuristic algorithm that has been widely used to solve optimization problems over the past four decades. In this article, we provide an overview of SA, including its origins, properties, relationship with statistical mechanics, and controlled parameters. We also present a practical application of SA to optimize a 2000-nodes system using the Ising model. Additionally, we introduce an advanced algorithm derived from SA, known as Quantum Annealing (QA). We explain the theory behind SA and provide an introduction to the background of the Coherent Ising Machine (CIM), which serves as a specific example of SA's application. In the future works section, we discuss QA and Adiabatic Quantum Computation (AQC) as a brief review and introduction