Freedom of mixer rotation-axis improves performance in the quantum approximate optimization algorithm

TL;DR

This paper introduces a modified QAOA with flexible mixer rotation axes, significantly enhancing solution quality for MAXCUT problems on small graphs and demonstrating potential for error mitigation and efficient problem-solving.

Contribution

The paper proposes a new QAOA variant with adjustable rotation axes in the mixer Hamiltonian, improving performance and error resilience in near-term quantum devices.

Findings

Drastic performance improvements over standard QAOA for MAXCUT on small graphs.

Enhanced error mitigation properties under realistic noise models.

Ability to solve certain problems in a single quantum round.

Abstract

Variational quantum algorithms such as the quantum approximate optimization algorithm (QAOA) are particularly attractive candidates for implementation on near-term quantum processors. As hardware realities such as error and qubit connectivity will constrain achievable circuit depth in the near future, new ways to achieve high-performance at low depth are of great interest. In this work, we present a modification to QAOA that adds additional variational parameters in the form of freedom of the rotation-axis in the $XY$-plane of the mixer Hamiltonian. Via numerical simulation, we show that this leads to a drastic performance improvement over standard QAOA at finding solutions to the MAXCUT problem on graphs of up to 7 qubits. Furthermore, we explore the Z-phase error mitigation properties of our modified ansatz, its performance under a realistic error model for a neutral atom quantum processor, and the class of problems it can solve in a single round.