I am currently doing some research that requires a quantum optimization algorithm. I have been looking at types of quantum algorithms to see which one would be most useful for my problem and expectations, and I have been specifically looking at QAOAs. On the paper https://arxiv.org/pdf/2306.09198.pdf there is a long list of QAOA types on page 62, but I don't understand what the paper means when it says that ma-QAOA achieves better approximation ratios and may require shallower circuits than a vanilla QAOA. Also, why does the addition of new parameters to include the angles of the "cost and mixer layers" affect the algorithm's performance? Is the ma-QAOA simply a better and more efficient algorithm to use?

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    $\begingroup$ The ma-QAOA can realize every vanilla QAOA circuit, plus more. But it also has more parameters to optimize over. You can always add parameters and get a “better” variational ansatz, with the caveat that if you do it too much you won’t be able to find the optimal parameters for your ansatz. This is why it is very hard to show QAOA “works” on any meaningful or provable level. $\endgroup$ Mar 17 at 3:13


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