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 Durr-Hoyer algorithm for quantum minimum finding (DH-QMF) that is based on Grovers 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.
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