  Calculating the expected spin configuration of the chain consisting of Ferrum
atoms interacting with each other through exchange interaction is fundamentally
a configuration optimization problem. Quantum Approximate Optimization
Algorithm is a suitable candidate to configure such systems on a quantum
device. In this work we have considered Ferrum chains of three different
lengths and calculated their most-probable spin configurations using Quantum
Approximate Optimization Algorithm. We employed a Quantum Feed Forward Neural
Network as the optimizer of Quantum Approximate Optimization Algorithm. We have
successfully obtained the expected spin configuration for the longest Ferrum
Chain.
