TY  - JOUR
AU  - Willsch, Madita
AU  - Willsch, Dennis
AU  - Jin, Fengping
AU  - De Raedt, Hans
AU  - Michielsen, Kristel
TI  - Benchmarking the quantum approximate optimization algorithm
JO  - Quantum information processing
VL  - 19
IS  - 7
SN  - 1570-0755
CY  - Dordrecht
PB  - Springer Science + Business Media B.V.
M1  - FZJ-2020-01888
SP  - 197
PY  - 2020
AB  - 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 are 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.
LB  - PUB:(DE-HGF)16
UR  - <Go to ISI:>//WOS:000538059200001
DO  - DOI:10.1007/s11128-020-02692-8
UR  - https://juser.fz-juelich.de/record/875239
ER  -