TY  - JOUR
AU  - Willsch, Dennis
AU  - Willsch, Madita
AU  - Jin, Fengping
AU  - Michielsen, Kristel
AU  - De Raedt, Hans
TI  - GPU-accelerated simulations of quantum annealing and the quantum approximate optimization algorithm
JO  - Computer physics communications
VL  - 278
SN  - 0010-4655
CY  - Amsterdam
PB  - North Holland Publ. Co.
M1  - FZJ-2022-04119
SP  - 108411
PY  - 2022
AB  - We study large-scale applications using a GPU-accelerated version of the massively parallel Jülich universal quantum computer simulator (JUQCS–G). First, we benchmark JUWELS Booster, a GPU cluster with 3744 NVIDIA A100 Tensor Core GPUs. Then, we use JUQCS–G to study the relation between quantum annealing (QA) and the quantum approximate optimization algorithm (QAOA). We find that a very coarsely discretized version of QA, termed approximate quantum annealing (AQA), performs surprisingly well in comparison to the QAOA. It can either be used to initialize the QAOA, or to avoid the costly optimization procedure altogether. Furthermore, we study the scaling of the success probability when using AQA for problems with 30 to 40 qubits. We find that the case with the largest discretization error scales most favorably, surpassing the best result obtained from the QAOA.
LB  - PUB:(DE-HGF)16
UR  - <Go to ISI:>//WOS:000831314600011
DO  - DOI:10.1016/j.cpc.2022.108411
UR  - https://juser.fz-juelich.de/record/910749
ER  -