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

Abstract

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