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Quantum alternating operator ansatz (QAOA) has a strong connection to theadiabatic algorithm, which it can approximate with sufficient depth. However,it is unclear to what extent the lessons from the adiabatic regime apply toQAOA as executed in practice with small to moderate depth. In this paper, wedemonstrate that the intuition from the adiabatic algorithm applies to the taskof choosing the QAOA initial state. Specifically, we observe that the bestperformance is obtained when the initial state of QAOA is set to be the groundstate of the mixing Hamiltonian, as required by the adiabatic algorithm. Weprovide numerical evidence using the examples of constrained portfoliooptimization problems with both low ($p\leq 3$) and high ($p = 100$) QAOAdepth. Additionally, we successfully apply QAOA with XY mixer to portfoliooptimization on a trapped-ion quantum processor using 32 qubits and discuss ourfindings in near-term experiments.

Alignment between Initial State and Mixer Improves QAOA Performance for Constrained Optimization