Numerical experiments on QAOA show optimal parameters often break expected patterns, performance becomes less parameter-sensitive with depth, and component-wise iterative fixing performs competitively or better at low depth.
To- ward a linear-ramp QAOA protocol: evidence of a scaling advantage in solving some combinatorial optimization problems
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Going off Pattern? QAOA Parameter Heuristics and Potentials of Parsimony
Numerical experiments on QAOA show optimal parameters often break expected patterns, performance becomes less parameter-sensitive with depth, and component-wise iterative fixing performs competitively or better at low depth.