Holography as deep learning
Holography as deep learning
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全息作为深度学习
DOI：
10.1142/s0218271817430209
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发表时间：
2017-05
影响因子：
2.2
通讯作者：
Shu Fu-Wen
中科院分区：
文献类型：
--
作者：
Gan Wen-Cong;Shu Fu-Wen
Quantum many-body problem with exponentially large degrees of freedom can be reduced to a tractable computational form by neural network method \cite{CT}. The power of deep neural network (DNN) based on deep learning is clarified by mapping it to renormalization group (RG), which may shed lights on holographic principle by identifying a sequence of RG transformations to the AdS geometry. In this essay, we show that any network which reflects RG process has intrinsic hyperbolic geometry, and discuss the structure of entanglement encoded in the graph of DNN. We find the entanglement structure of deep neural network is of Ryu-Takayanagi form. Based on these facts, we argue that the emergence of holographic gravitational theory is related to deep learning process of the quantum field theory.
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影响因子：
16.6
作者：
Gao X;Duan LM
通讯作者：
Duan LM
DOI：
10.1103/physrevb.94.165134
发表时间：
2016-06
期刊：
ArXiv
影响因子：
--
作者：
G. Torlai;R. Melko
通讯作者：
G. Torlai;R. Melko
影响因子：
56.9
作者：
Carleo, Giuseppe;Troyer, Matthias
通讯作者：
Troyer, Matthias
影响因子：
1.7
作者：
J. McGreevy
通讯作者：
J. McGreevy
影响因子：
1.3
作者：
SUSSKIND, L
通讯作者：
SUSSKIND, L