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Quantum Optimization

Performance Analysis of Multi-Angle QAOA for p > 1

arXiv

Authors: Igor Gaidai, Rebekah Herrman

Year

2023

Paper ID

6386

Status

Preprint

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~2 min

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Abstract

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.

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This paper contributes to the Quantum Optimization research area in the Quantum Articles archive.
It adds a 2023 reference point for readers tracking recent quantum research.
In this paper we consider the scalability of Multi-Angle QAOA with respect to the number of QAOA layers.

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References & Citation Signals

[1] DOI https://doi.org/arXiv:2312.00200 [2] arXiv https://arxiv.org/abs/2312.00200 [3] Publisher https://arxiv.org/abs/2312.00200

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