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Variational quantum algorithms such as the quantum approximate optimizationalgorithm (QAOA) are particularly attractive candidates for implementation onnear-term quantum processors. As hardware realities such as error and qubitconnectivity will constrain achievable circuit depth in the near future, newways to achieve high-performance at low depth are of great interest. In thiswork, we present a modification to QAOA that adds additional variationalparameters in the form of freedom of the rotation-axis in the $XY$-plane of themixer Hamiltonian. Via numerical simulation, we show that this leads to adrastic performance improvement over standard QAOA at finding solutions to theMAXCUT problem on graphs of up to 7 qubits. Furthermore, we explore the Z-phaseerror mitigation properties of our modified ansatz, its performance under arealistic error model for a neutral atom quantum processor, and the class ofproblems it can solve in a single round.

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