  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.
