GPU-accelerated simulations of quantum annealing and the quantum approximate optimization algorithm

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 anne...

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Vydané v:Computer physics communications Ročník 278; s. 108411
Hlavní autori: Willsch, Dennis, Willsch, Madita, Jin, Fengping, Michielsen, Kristel, De Raedt, Hans
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Elsevier B.V 01.09.2022
Predmet:
Approximate quantum annealing
Computer simulation
High performance computing
Parallelization
QAOA
Quantum annealing
Quantum computing
Quantum computing
Approximate quantum annealing
High performance computing
Computer simulation
QAOA
Quantum annealing
Parallelization
ISSN:0010-4655, 1879-2944
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Popis
Shrnutí: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.
ISSN:0010-4655
1879-2944
DOI:10.1016/j.cpc.2022.108411