Augmenting QAOA Ansatz with Multiparameter Problem-Independent Layer

TL;DR

This paper introduces QAOA+, an enhanced quantum algorithm for combinatorial optimization that adds a problem-independent layer to improve approximation ratios without increasing circuit depth.

Contribution

The paper proposes QAOA+, a novel ansatz that outperforms standard QAOA and other multiangle variants by adding a multiparameter layer while maintaining low circuit depth.

Findings

QAOA+ achieves higher approximation ratios than p=1 QAOA.

QAOA+ maintains circuit depth below p=2 QAOA.

QAOA+ outperforms alternative multiangle QAOA ans"atze.

Abstract

The quantum approximate optimization algorithm (QAOA) promises to solve classically intractable computational problems in the area of combinatorial optimization. A growing amount of evidence suggests that the originally proposed form of the QAOA ansatz is not optimal, however. To address this problem, we propose an alternative ansatz, which we call QAOA+, that augments the traditional $p = 1$ QAOA ansatz with an additional multiparameter problem-independent layer. The QAOA+ ansatz allows obtaining higher approximation ratios than $p = 1$ QAOA while keeping the circuit depth below that of $p = 2$ QAOA, as benchmarked on the MaxCut problem for random regular graphs. We additionally show that the proposed QAOA+ ansatz, while using a larger number of trainable classical parameters than with the standard QAOA, in most cases outperforms alternative multiangle QAOA ans\"atze.