Benchmarking the Quantum Approximate Optimization Algorithm
Abstract
The performance of the quantum approximate optimization algorithm is evaluated by using three different measures: the probability of finding the ground state, the energy expectation value, and a ratio closely related to the approximation ratio. The set of problem instances studied consists of weighted MaxCut problems and 2-satisfiability problems. The Ising model representations of the latter possess unique ground states and highly-degenerate first excited states. The quantum approximate optimization algorithm is executed on quantum computer simulators and on the IBM Q Experience. Additionally, data obtained from the D-Wave 2000Q quantum annealer is used for comparison, and it is found that the D-Wave machine outperforms the quantum approximate optimization algorithm executed on a simulator. The overall performance of the quantum approximate optimization algorithm is found to strongly depend on the problem instance.
Cite
@article{arxiv.1907.02359,
title = {Benchmarking the Quantum Approximate Optimization Algorithm},
author = {Madita Willsch and Dennis Willsch and Fengping Jin and Hans De Raedt and Kristel Michielsen},
journal= {arXiv preprint arXiv:1907.02359},
year = {2020}
}
Comments
Version after the referee process. Published in Quantum Information Processing