The code here is a modification from https://lucaman99.github.io/new_blog/2020/mar16.html In order to understand the code and the idea being approximating Maxcut using Quantum Approximate Optimization Algorithm (QAOA), I recomment you read this amzing guide from Musty thoughts. However, you would need basics from Variational Quatnum Eigensolver (VQE). Moreover, I would urge you to read this awe-inspring essay while will give you some basis (pun intended!) to better comprehend the content of the aforementioned blogposts. Furthermore, if you are not familiar with Linear Algebra, I recomment you pause here, revise it, learn it, and come back!
This code has a number of functions which would aid in estimating the ground state of a Hamiltonian of the cost function. The cost function is minimized to do just that.
The first step in making use of this code is to declare a graph with some edges and defined weights, like this: A square graph would look like this The graph would contain a set of edges \n
set_edges = [E(0,1,10), E(1,2,1), E(2,3,10),E(3,0,10)]
graph_ = G(set_edges)
**call the approximate_maxcut function defined in the code and pass the declared graph as a parameter
approximate_maxcut(graph_)
The final printed line is in the form |0010> where each bit represent each node in either being S or S'