Approximate quantum annealing supplies effective warm-start parameters for QAOA while EHQO guides optimization through intermediate Hamiltonians, yielding better performance than random initialization on hard 2-SAT problems in numerical tests.
However, it was found that while this choice performed well for the Nelder-Mead optimizer, it consistently caused the BFGS op- timization to stall
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Quantum annealing inspired algorithms for the NISQ Era
Approximate quantum annealing supplies effective warm-start parameters for QAOA while EHQO guides optimization through intermediate Hamiltonians, yielding better performance than random initialization on hard 2-SAT problems in numerical tests.