| Network-Initialized Monte Carlo Based on Generative Neural Networks |
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| 作者姓名： | 卢虹宇 李楚豪 陈斌斌 李伟 戚扬 孟子杨 |
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| 作者单位： | 1. Department of Physics and HKU-UCAS Joint Institute of Theoretical and Computational Physics,The University of Hong Kong;2. Beijing National Laboratory for Condensed Matter Physics, and Institute of Physics,Chinese Academy of Sciences;3. School of Physical Sciences, University of Chinese Academy of Sciences;4. Institute of Theoretical Physics, Chinese Academy of Sciences;5. School of Physics, Beihang University;6. State Key Laboratory of Surface Physics, Fudan University;7. Center for Field Theory and Particle Physics, Department of Physics, Fudan University |
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| 基金项目： | support from the RGC of Hong Kong SAR of China (Grant Nos. 17303019, 17301420, 17301721, and Ao E/P-701/20)；;the National Natural Science Foundation of China (Grant Nos. 11974036, 11874115, and 11834014)；;the K.C.Wong Education Foundation (Grant No. GJTD-2020-01)； |
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| 摘 要： | We design generative neural networks that generate Monte Carlo configurations with complete absence of autocorrelation from which only short Markov chains are needed before making measurements for physical observables,irrespective of the system locating at the classical critical point,fermionic Mott insulator,Dirac semimetal,or quantum critical point.We further propose a network-initialized Monte Carlo scheme based on such neural networks,which provides independent samplings and can accelerate t...
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