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000910749 1001_ $$0P:(DE-Juel1)167542$$aWillsch, Dennis$$b0$$eCorresponding author$$ufzj
000910749 245__ $$aGPU-accelerated simulations of quantum annealing and the quantum approximate optimization algorithm
000910749 260__ $$aAmsterdam$$bNorth Holland Publ. Co.$$c2022
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000910749 520__ $$aWe 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.
000910749 536__ $$0G:(DE-HGF)POF4-5111$$a5111 - Domain-Specific Simulation & Data Life Cycle Labs (SDLs) and Research Groups (POF4-511)$$cPOF4-511$$fPOF IV$$x0
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000910749 7001_ $$0P:(DE-Juel1)167543$$aWillsch, Madita$$b1$$ufzj
000910749 7001_ $$0P:(DE-Juel1)144355$$aJin, Fengping$$b2$$ufzj
000910749 7001_ $$0P:(DE-Juel1)138295$$aMichielsen, Kristel$$b3$$ufzj
000910749 7001_ $$0P:(DE-Juel1)179169$$aDe Raedt, Hans$$b4$$ufzj
000910749 773__ $$0PERI:(DE-600)1466511-6$$a10.1016/j.cpc.2022.108411$$gVol. 278, p. 108411 -$$p108411$$tComputer physics communications$$v278$$x0010-4655$$y2022
000910749 8564_ $$uhttps://juser.fz-juelich.de/record/910749/files/Invoice_OAD0000207776.pdf
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000910749 9141_ $$y2022
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