Journal Article

FZJ-2022-04119

http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png GPU-accelerated simulations of quantum annealing and the quantum approximate optimization algorithm

Willsch, D. (Corresponding author)FZJ* ; Willsch, M.FZJ* ; Jin, F.FZJ* ; Michielsen, K.FZJ* ; De Raedt, H.FZJ*

2022
North Holland Publ. Co. Amsterdam

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Please use a persistent id in citations: http://hdl.handle.net/2128/32331  doi:10.1016/j.cpc.2022.108411

Abstract: 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.

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Contributing Institute(s):

1. Jülich Supercomputing Center (JSC)

Research Program(s):

1. 5111 - Domain-Specific Simulation & Data Life Cycle Labs (SDLs) and Research Groups (POF4-511) (POF4-511)

Appears in the scientific report 2022

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Database coverage:
; ; ; Clarivate Analytics Master Journal List ; Current Contents - Physical, Chemical and Earth Sciences ; Ebsco Academic Search ; Essential Science Indicators ; IF < 5 ; JCR ; Nationallizenz ; SCOPUS ; Science Citation Index Expanded ; Web of Science Core Collection

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