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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| Published in | Computer physics communications Vol. 278; p. 108411 |
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| Main Authors | , , , , |
| Format | Journal Article |
| Language | English |
| Published |
Elsevier B.V
01.09.2022
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| Subjects | |
| Online Access | Get full text |
| ISSN | 0010-4655 1879-2944 1879-2944 |
| DOI | 10.1016/j.cpc.2022.108411 |
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| 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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| AbstractList | 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. |
| ArticleNumber | 108411 |
| Author | Willsch, Dennis Jin, Fengping De Raedt, Hans Willsch, Madita Michielsen, Kristel |
| Author_xml | – sequence: 1 givenname: Dennis orcidid: 0000-0003-3855-5100 surname: Willsch fullname: Willsch, Dennis email: d.willsch@fz-juelich.de organization: Institute for Advanced Simulation, Jülich Supercomputing Centre, Forschungszentrum Jülich, 52425 Jülich, Germany – sequence: 2 givenname: Madita orcidid: 0000-0002-2351-3162 surname: Willsch fullname: Willsch, Madita organization: Institute for Advanced Simulation, Jülich Supercomputing Centre, Forschungszentrum Jülich, 52425 Jülich, Germany – sequence: 3 givenname: Fengping surname: Jin fullname: Jin, Fengping organization: Institute for Advanced Simulation, Jülich Supercomputing Centre, Forschungszentrum Jülich, 52425 Jülich, Germany – sequence: 4 givenname: Kristel orcidid: 0000-0003-1444-4262 surname: Michielsen fullname: Michielsen, Kristel organization: Institute for Advanced Simulation, Jülich Supercomputing Centre, Forschungszentrum Jülich, 52425 Jülich, Germany – sequence: 5 givenname: Hans surname: De Raedt fullname: De Raedt, Hans organization: Institute for Advanced Simulation, Jülich Supercomputing Centre, Forschungszentrum Jülich, 52425 Jülich, Germany |
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| Keywords | Quantum computing Approximate quantum annealing High performance computing Computer simulation QAOA Quantum annealing Parallelization |
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2 Apolloni (10.1016/j.cpc.2022.108411_br0250) 1989; 33 Message Passing Interface Forum (10.1016/j.cpc.2022.108411_br0960) 2021 Sieberer (10.1016/j.cpc.2022.108411_br0460) 2019; 5 Bian (10.1016/j.cpc.2022.108411_br0670) 2014; 2 Morales (10.1016/j.cpc.2022.108411_br0900) 2011; 38 Ernst (10.1016/j.cpc.2022.108411_br0970) 2004; 153 Wang (10.1016/j.cpc.2022.108411_br0100) 2018; 97 Phillipson (10.1016/j.cpc.2022.108411_br0370) 2021; 10 Nielsen (10.1016/j.cpc.2022.108411_br0340) 2010 Pednault (10.1016/j.cpc.2022.108411_br0540) Willsch (10.1016/j.cpc.2022.108411_br0130) 2020; 19 Bengtsson (10.1016/j.cpc.2022.108411_br0150) 2020; 14 Medvidović (10.1016/j.cpc.2022.108411_br0220) 2021; 7 Nelder (10.1016/j.cpc.2022.108411_br0880) 1965; 7 Fernández-Pendás (10.1016/j.cpc.2022.108411_br0210) 2021; 113388 Kadowaki (10.1016/j.cpc.2022.108411_br0270) 1998; 58 Vikstål (10.1016/j.cpc.2022.108411_br0140) 2020; 14 Job (10.1016/j.cpc.2022.108411_br0320) 2018; 3 Rahman (10.1016/j.cpc.2022.108411_br0410) Suarez (10.1016/j.cpc.2022.108411_br0020) 2020; vol. 50 Sax (10.1016/j.cpc.2022.108411_br0830) 2020 De Raedt (10.1016/j.cpc.2022.108411_br0940) Boixo (10.1016/j.cpc.2022.108411_br0550) Streif (10.1016/j.cpc.2022.108411_br0230) 2020; 5 Hadfield (10.1016/j.cpc.2022.108411_br0730) 2019; 12 De Raedt (10.1016/j.cpc.2022.108411_br0030) 2007; 176 Cohen (10.1016/j.cpc.2022.108411_br0390) Chen (10.1016/j.cpc.2022.108411_br0560) 2018; 964 Pearson (10.1016/j.cpc.2022.108411_br0710) 2019; 5 Harris (10.1016/j.cpc.2022.108411_br0290) 2010; 82 Willsch (10.1016/j.cpc.2022.108411_br0990) 2020; 101 De Raedt (10.1016/j.cpc.2022.108411_br0870) 2006 Deutsch (10.1016/j.cpc.2022.108411_br0520) 1995; 449 Trotter (10.1016/j.cpc.2022.108411_br0470) 1959; 10 De Raedt (10.1016/j.cpc.2022.108411_br0040) 2019; 237 Farhi (10.1016/j.cpc.2022.108411_br0770) Press (10.1016/j.cpc.2022.108411_br0910) 2007 Hsu (10.1016/j.cpc.2022.108411_br0920) 2019; 26 Bhatia (10.1016/j.cpc.2022.108411_br0360) 2021 Childs (10.1016/j.cpc.2022.108411_br0650) 2001; 65 |
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| SubjectTerms | Approximate quantum annealing Computer simulation High performance computing Parallelization QAOA Quantum annealing Quantum computing |
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| Title | GPU-accelerated simulations of quantum annealing and the quantum approximate optimization algorithm |
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