In this paper we consider the scalability of Multi-Angle QAOA with respect to the number of QAOA layers. We found that MA-QAOA is able to significantly reduce the depth of QAOA circuits, by a factor of up to 4 for the considered data sets. However, MA-QAOA is not optimal for minimization of the total QPU time. Different optimization initialization strategies are considered and compared for both QAOA and MA-QAOA. Among them, a new initialization strategy is suggested for MA-QAOA that is able to consistently and significantly outperform random initialization used in the previous studies.
Cite
@article{arxiv.2312.00200,
title = {Performance Analysis of Multi-Angle QAOA for p > 1},
author = {Igor Gaidai and Rebekah Herrman},
journal= {arXiv preprint arXiv:2312.00200},
year = {2024}
}