QAOAKit: A Toolkit for Reproducible Study, Application, and Verification of the QAOA

TL;DR

QAOAKit is a Python toolkit designed to facilitate reproducible research, application, and verification of the Quantum Approximate Optimization Algorithm (QAOA) by providing standardized parameters, circuit generators, and cross-framework compatibility.

Contribution

It introduces a unified, validated repository of QAOA parameters and tools for cross-platform use, enhancing reproducibility and research in quantum optimization.

Findings

Standardized and validated QAOA parameters for MaxCut

Tools for reproducing and extending quantum optimization results

Enhanced cross-framework compatibility for QAOA simulations

Abstract

Understanding the best known parameters, performance, and systematic behavior of the Quantum Approximate Optimization Algorithm (QAOA) remain open research questions, even as the algorithm gains popularity. We introduce QAOAKit, a Python toolkit for the QAOA built for exploratory research. QAOAKit is a unified repository of preoptimized QAOA parameters and circuit generators for common quantum simulation frameworks. We combine, standardize, and cross-validate previously known parameters for the MaxCut problem, and incorporate this into QAOAKit. We also build conversion tools to use these parameters as inputs in several quantum simulation frameworks that can be used to reproduce, compare, and extend known results from various sources in the literature. We describe QAOAKit and provide examples of how it can be used to reproduce research results and tackle open problems in quantum optimization.