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.
@article{arxiv.2107.02789,
title = {Digitized-counterdiabatic quantum approximate optimization algorithm},
author = {P. Chandarana and N. N. Hegade and K. Paul and F. Albarrán-Arriagada and E. Solano and A. del Campo and Xi Chen},
journal= {arXiv preprint arXiv:2107.02789},
year = {2022}
}