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arXiv · 2107.02789

Digitized-counterdiabatic quantum approximate optimization algorithm

P. Chandarana·N. N. Hegade·K. Paul·F. Albarrán-Arriagada·E. Solano·A. del Campo·Xi Chen
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Abstract

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.

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P. Chandarana, N. N. Hegade, K. Paul, F. Albarrán-Arriagada, E. Solano, A. del Campo, Xi Chen. 2021-07-06. Digitized-counterdiabatic quantum approximate optimization algorithm. https://doi.org/10.1103/physrevresearch.4.013141

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