Searching for Possible Spin Configurations of Ferrum Chain via Quantum Approximate Optimization Algorithm

TL;DR

This paper explores using the Quantum Approximate Optimization Algorithm with a Quantum Feed Forward Neural Network to determine the most probable spin configurations in Ferrum atom chains of varying lengths, demonstrating success on longer chains.

Contribution

It introduces a novel approach combining QAOA with a quantum neural network optimizer to analyze spin configurations in Ferrum chains.

Findings

Successfully obtained spin configurations for the longest chain

Demonstrated effectiveness of QAOA with neural network optimizer

Applied method to chains of three different lengths

Abstract

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.