| Predicting Quantum Many-Body Dynamics with Transferable Neural Net works |
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| 作者姓名： | 张泽旺 杨硕 吴亦航 刘晨曦 韩翊民 Ching-Hua Lee 孙政 李光杰 张笑 |
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| 作者单位： | School of Physics;State Key Laboratory of Low-Dimensional Quantum Physics and Department of Physics;Department of Physics;Institute of High Performance Computing |
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| 基金项目： | the National Natural Science Foundation of China under Grant Nos 11874431 and 11804181;the National Key R&D Program of China under Grant No 2018YFA0306800;the Guangdong Science and Technology Innovation Youth Talent Program under Grant Nos 2016TQ03X688 and 2018YFA0306504;the Research Fund Program of the State Key Laboratory of Low-Dimensional Quantum Physics under Grant No ZZ201803. |
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| 摘 要： | Advanced machine learning(ML)approaches such as transfer learning have seldom been applied to approximate quantum many-body systems.Here we demonstrate that a simple recurrent unit(SRU)based efficient and transferable sequence learning framework is capable of learning and accurately predicting the time evolution of the one-dimensional(ID)Ising model with simultaneous transverse and parallel magnetic fields,as quantitatively corroborated by relative entropy measurements between the predicted and exact state distributions.At a cost of constant computational complexity,a larger many-body state evolution is predicted in an autoregressive way from just one initial state,without any guidance or knowledge of any Hamiltonian.Our work paves the way for future applications of advanced ML methods in quantum many-body dynamics with knowledge only from a smaller system.
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| 关 键 词： | QUANTUM EXACT ENTROPY |
Predicting Quantum Many-Body Dynamics with Transferable Neural Networks |
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| Ze-Wang Zhang,Shuo Yang,Yi-Hang Wu,Chen-Xi Liu,Yi-Min Han,Ching-Hua Lee,Zheng Sun,Guang-Jie Li,Xiao Zhang.Predicting Quantum Many-Body Dynamics with Transferable Neural Net works[J].Chinese Physics Letters,2020(1):101-104. |
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| Authors: | Ze-Wang Zhang Shuo Yang Yi-Hang Wu Chen-Xi Liu Yi-Min Han Ching-Hua Lee Zheng Sun Guang-Jie Li Xiao Zhang |
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| Institution: | (School of Physics,Sun Yat-sen University,Guangzhou 510275;State Key Laboratory of Low-Dimensional Quantum Physics and Department of Physics,Tsinghua University,Beijing 100084;Department of Physics,National University of Singapore,117542,Singapore;Institute of High Performance Computing,138632,Singapore) |
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| Abstract: | Advanced machine learning(ML) approaches such as transfer learning have seldom been applied to approximate quantum many-body systems.Here we demonstrate that a simple recurrent unit(SRU) based efficient and transferable sequence learning framework is capable of learning and accurately predicting the time evolution of the one-dimensional(1 D) Ising model with simultaneous transverse and parallel magnetic fields,as quantitatively corroborated by relative entropy measurements between the predicted and exact state distributions.At a cost of constant computational complexity,a larger many-body state evolution is predicted in an autoregressive way from just one initial state,without any guidance or knowledge of any Hamiltonian.Our work paves the way for future applications of advanced ML methods in quantum many-body dynamics with knowledge only from a smaller system. |
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