  We study the performance scaling of three quantum algorithms for
combinatorial optimization: measurement-feedback coherent Ising machines
(MFB-CIM), discrete adiabatic quantum computation (DAQC), and the D\"urr-Hoyer
algorithm for quantum minimum finding (DH-QMF) that is based on Grover's
search. We use MaxCut problems as our reference for comparison, and
time-to-solution (TTS) as a practical measure of performance for these
optimization algorithms. We empirically observe a $\Theta(2^{\sqrt{n}})$
scaling for the median TTS for MFB-CIM, in comparison to the exponential
scaling with the exponent $n$ for DAQC and the provable $\widetilde{\mathcal
O}\left(\sqrt{2^n}\right)$ scaling for DH-QMF. We conclude that these scaling
complexities result in a dramatic performance advantage for MFB-CIM in
comparison to the other two algorithms for solving MaxCut problems.
