Performance Analysis of Multi-Angle QAOA for p > 1

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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• Quantum approximate optimization algorithm with random and subgraph phase operators
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• Efficient Large-Scale Quantum Optimization via Counterdiabatic Ansatz
• A Cyclic Layerwise QAOA Training
• Exploring Entanglement and Parameter Sensitivity in QAOA through Quantum Fisher Information