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QAOAKit: A Toolkit for Reproducible Study, Application, and Verification of the QAOA
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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.
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Landscape-Similarity-Guided Optimization in Divide-and-Conquer QAOA
DO-QAOA shows that the 2^m subproblems in frozen-qubit divide-and-conquer QAOA share near-identical variational landscapes, so training one representative and transferring its parameters cuts training cost from expone...
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