Solving MaxCut problem with Quantunum Approximate Optimization Algorithm

Abstract

Given an undirected unweighted graph, the 2-MaxCut problem can be stated as the problem of partitioning the nodes of a graph into two subsets such that the number of edges between them is as large as possible. It is a well-studied NP-hard and APX-hard problem with applications in various fields, including statistical physics, machine learning and circuit layout design. This thesis investigates the Quantum Approximation Optimization Algorithm for solving the MaxCut problem. We study this approach analytically, show how to implement it on the quantum circuit, hold the experiments on the quantum simulator and the real quantum computer and test how good this algorithm works on graphs of different sizes.