Introductory Tutorial for SPSA and the Quantum Approximation Optimization Algorithm

TL;DR

This tutorial introduces how to implement the Quantum Approximation Optimization Algorithm (QAOA) combined with SPSA for solving Max-Cut, emphasizing practical setup with clear, code-based examples in R and Python.

Contribution

It provides a practical, step-by-step guide to implementing QAOA with SPSA, including fully functional code examples, without relying on complex theory.

Findings

Practical implementation steps for QAOA with SPSA

Code examples in R and Python using Qiskit

Accessible tutorial without complex mathematics

Abstract

This short tutorial provides an introduction to the Quantum Approximation Optimization Algorithm (QAOA). Specifically, how to use QAOA with the Simultaneous Perturbation Stochastic Approximation (SPSA) algorithm to solve the Max-Cut problem. All steps of the algorithm are explicitly shown and no theory or complex mathematics are used. The focus is entirely on setting up a practical implementation. Fully functional examples in both R and python (using Qiskit) are provided, both using roughly 100 lines of code.