Iteratively reusing the best measured solutions as a superposed warm-start state improves QAOA results on MaxCut and portfolio optimization in small-scale simulations.
Warm-started QAOA with Custom Mixers Provably Converges and Computationally Beats Goemans-Williamson’s Max-Cut at Low Circuit Depths
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Iterative quantum optimisation with a warm-started quantum state
Iteratively reusing the best measured solutions as a superposed warm-start state improves QAOA results on MaxCut and portfolio optimization in small-scale simulations.