Digitized-counterdiabatic quantum approximate optimization algorithm

TL;DR

This paper introduces a digitized-counterdiabatic QAOA that incorporates counterdiabatic driving to improve optimization performance across various quantum and classical problems.

Contribution

It proposes a novel digitized QAOA framework enhanced with counterdiabatic terms, improving upon standard QAOA for multiple problem types.

Findings

Outperforms standard QAOA in Ising models

Achieves better results on classical optimization problems

Demonstrates effectiveness on P-spin model

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.