Related references
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Article
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Summary: The quantum approximate optimization algorithm (QAOA) is studied in relation to adiabaticity, and the connection is made explicit by constructing counterdiabatic (CD) evolution. By linking QAOA with quantum adiabatic algorithms (QAA), the convergence of the approximation ratio is shown, and the optimization of CD-QAOA angles is found to be equivalent to optimizing a continuous adiabatic schedule. It is demonstrated that QAOA is at least counterdiabatic and has better performance than finite time adiabatic evolution.
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Summary: This paper develops an iterative version of the quantum approximate optimization algorithm (QAOA) that is problem tailored and can be adapted to specific hardware constraints. The algorithm is simulated on a class of Max-Cut graph problems and shows faster convergence compared to the standard QAOA, while reducing the required number of gates and optimization parameters.
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Optics
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Summary: This study presents numerical evidence for the fixed angle conjecture for QAOA, showing that at certain fixed angles, QAOA can have universally good performance on any 3-regular graph, even outperforming other algorithms.
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Review
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