Deep learning representations for quantum many-body systems on heterogeneous hardware
The quantum many-body problems are important for condensed matter physics, however solving the problems are challenging because the Hilbert space grows exponentially with the size of the problem. The recently developed deep learning methods provide a promising new route to solve long-standing quantu...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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IOP Publishing
2023-01-01
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| Series: | Machine Learning: Science and Technology |
| Subjects: | |
| Online Access: | https://doi.org/10.1088/2632-2153/acc56a |
| _version_ | 1797844571528364032 |
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| author | Xiao Liang Mingfan Li Qian Xiao Junshi Chen Chao Yang Hong An Lixin He |
| author_facet | Xiao Liang Mingfan Li Qian Xiao Junshi Chen Chao Yang Hong An Lixin He |
| author_sort | Xiao Liang |
| collection | DOAJ |
| description | The quantum many-body problems are important for condensed matter physics, however solving the problems are challenging because the Hilbert space grows exponentially with the size of the problem. The recently developed deep learning methods provide a promising new route to solve long-standing quantum many-body problems. We report that a deep learning based simulation can achieve solutions with competitive precision for the spin $J1$ – $J2$ model and fermionic t - J model, on rectangular lattices within periodic boundary conditions. The optimizations of the deep neural networks are performed on the heterogeneous platforms, such as the new generation Sunway supercomputer and the multi graphical-processing-unit clusters. Both high scalability and high performance are achieved within an AI-HPC hybrid framework. The accomplishment of this work opens the door to simulate spin and fermionic lattice models with state-of-the-art lattice size and precision. |
| first_indexed | 2024-04-09T17:24:28Z |
| format | Article |
| id | doaj.art-07f3871e6f00495b93393adbfa644065 |
| institution | Directory Open Access Journal |
| issn | 2632-2153 |
| language | English |
| last_indexed | 2024-04-09T17:24:28Z |
| publishDate | 2023-01-01 |
| publisher | IOP Publishing |
| record_format | Article |
| series | Machine Learning: Science and Technology |
| spelling | doaj.art-07f3871e6f00495b93393adbfa6440652023-04-18T13:52:29ZengIOP PublishingMachine Learning: Science and Technology2632-21532023-01-014101503510.1088/2632-2153/acc56aDeep learning representations for quantum many-body systems on heterogeneous hardwareXiao Liang0https://orcid.org/0000-0002-7882-3571Mingfan Li1Qian Xiao2Junshi Chen3Chao Yang4Hong An5Lixin He6https://orcid.org/0000-0003-2050-134XCAS Key Lab of Quantum Information, University of Science and Technology of China , Hefei, People’s Republic of ChinaSchool of Computer Science and Technology, University of Science and Technology of China , Hefei, People’s Republic of ChinaSchool of Computer Science and Technology, University of Science and Technology of China , Hefei, People’s Republic of ChinaSchool of Computer Science and Technology, University of Science and Technology of China , Hefei, People’s Republic of ChinaSchool of Mathematical Sciences, Peking University , Beijing, People’s Republic of ChinaSchool of Computer Science and Technology, University of Science and Technology of China , Hefei, People’s Republic of ChinaCAS Key Lab of Quantum Information, University of Science and Technology of China , Hefei, People’s Republic of China; Institute of Artificial Intelligence, Hefei Comprehensive National Science Center , Hefei, People’s Republic of ChinaThe quantum many-body problems are important for condensed matter physics, however solving the problems are challenging because the Hilbert space grows exponentially with the size of the problem. The recently developed deep learning methods provide a promising new route to solve long-standing quantum many-body problems. We report that a deep learning based simulation can achieve solutions with competitive precision for the spin $J1$ – $J2$ model and fermionic t - J model, on rectangular lattices within periodic boundary conditions. The optimizations of the deep neural networks are performed on the heterogeneous platforms, such as the new generation Sunway supercomputer and the multi graphical-processing-unit clusters. Both high scalability and high performance are achieved within an AI-HPC hybrid framework. The accomplishment of this work opens the door to simulate spin and fermionic lattice models with state-of-the-art lattice size and precision.https://doi.org/10.1088/2632-2153/acc56aneural network quantum statedeep learningstochastic reconfigurationLanczosSunwayheterogeneous architecture |
| spellingShingle | Xiao Liang Mingfan Li Qian Xiao Junshi Chen Chao Yang Hong An Lixin He Deep learning representations for quantum many-body systems on heterogeneous hardware Machine Learning: Science and Technology neural network quantum state deep learning stochastic reconfiguration Lanczos Sunway heterogeneous architecture |
| title | Deep learning representations for quantum many-body systems on heterogeneous hardware |
| title_full | Deep learning representations for quantum many-body systems on heterogeneous hardware |
| title_fullStr | Deep learning representations for quantum many-body systems on heterogeneous hardware |
| title_full_unstemmed | Deep learning representations for quantum many-body systems on heterogeneous hardware |
| title_short | Deep learning representations for quantum many-body systems on heterogeneous hardware |
| title_sort | deep learning representations for quantum many body systems on heterogeneous hardware |
| topic | neural network quantum state deep learning stochastic reconfiguration Lanczos Sunway heterogeneous architecture |
| url | https://doi.org/10.1088/2632-2153/acc56a |
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