Speaker
Description
The Quantum Approximate Optimization Algorithm (QAOA)[1]
is one of the leading variational quantum algorithms used to prepare
the ground state of gauge theories. The design of QAOA is under-
pinned by the adiabatic theorem. Recently, there has been a proposed
variation of QAOA, DC-QAOA[2], that incorporates counterdiabatic
driving in order to speed up the adiabatic process, leading to reduced
circuit depths and runtimes. In this work, we use QAOA and DC-
QAOA, along with other counterdiabatic variations[3, 4], to find the
ground state of the Schwinger model and compare the results to show
how counterdiabatic driving can be used to improve the ground state
preparation of gauge theories.
References:
[1] Edward Farhi, Jeffrey Goldstone, and Sam Gutmann. A quantum ap-
proximate optimization algorithm, 2014.
[2] P. Chandarana, N. N. Hegade, K. Paul, F. Albarran-Arriagada, E. Solano,
A. del Campo, and Xi Chen. Digitized-counterdiabatic quantum approx-
imate optimization algorithm. Physical Review Research, 4(1), February
2022.
[3] Pranav Chandarana, Narendra N. Hegade, Iraitz Montalban, Enrique
Solano, and Xi Chen. Digitized counterdiabatic quantum algorithm for
protein folding. Physical Review Applied, 20(1), 2023
[4] Ruoqian Xu, Sebastian V. Romero, Jialiang Tang, Yue Ban, and Xi Chen.
Digitized counterdiabatic quantum optimization for bin packing problem.
EPJ Quantum Technology, 12(1), August 2025.