  The quantum approximate optimization algorithm (QAOA) has proved to be an
effective classical-quantum algorithm serving multiple purposes, from solving
combinatorial optimization problems to finding the ground state of many-body
quantum systems. Since QAOA is an ansatz-dependent algorithm, there is always a
need to design ansatz for better optimization. To this end, we propose a
digitized version of QAOA enhanced via the use of shortcuts to adiabaticity.
Specifically, we use a counterdiabatic (CD) driving term to design a better
ansatz, along with the Hamiltonian and mixing terms, enhancing the global
performance. We apply our digitized-counterdiabatic QAOA to Ising models,
classical optimization problems, and the P-spin model, demonstrating that it
outperforms standard QAOA in all cases we study.
