Classical feedback-based optimization matches or exceeds quantum performance in speed and scalability while quantum retains an edge in final solution quality on tested instances.
The single parameterβ X(t) is given by βX(t) = i⟨Ψ(t)| " ˆHP, NX i=1 ˆXi # |Ψ(t)⟩,(6) which guarantees the reduction of the cost function (4)
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Feedback-based quantum optimization and its classical counterpart: quantum advantage and the power of classical algorithms
Classical feedback-based optimization matches or exceeds quantum performance in speed and scalability while quantum retains an edge in final solution quality on tested instances.