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Bridging Many-Body Quantum Physics and Deep Learning via Tensor Networks

Yoav Levine, Or Sharir, Nadav Cohen, Amnon Shashua
(Submitted on 26 Mar 2018 (v1), last revised 5 Apr 2018 (this version, v2))

The harnessing of modern computational abilities for many-body wave-function representations is naturally placed as a prominent avenue in contemporary condensed matter physics. Specifically, highly expressive computational schemes that are able to efficiently represent the entanglement properties of many-particle systems are of interest. In the seemingly unrelated field of machine learning, deep network architectures have exhibited an unprecedented ability to tractably encompass the dependencies characterizing hard learning tasks such as image classification. However, key questions regarding deep learning architecture design still have no adequate theoretical answers. In this paper, we establish a Tensor Network (TN) based common language between the two disciplines, which allows us to offer bidirectional contributions. By showing that many-body wave-functions are structurally equivalent to mappings of ConvACs and RACs, we construct their TN equivalents, and suggest quantum entanglement measures as natural quantifiers of dependencies in such networks. Accordingly, we propose a novel entanglement based deep learning design scheme. In the other direction, we identify that an inherent re-use of information in state-of-the-art deep learning architectures is a key trait that distinguishes them from standard TNs. Therefore, we employ a TN manifestation of information re-use and construct TNs corresponding to powerful architectures such as deep recurrent and overlapping convolutional networks. This allows us to demonstrate that the entanglement scaling supported by state-of-the-art deep learning architectures matches that of MERA TN in 1D, and that they support volume law entanglement in 2D polynomially more efficiently than RBMs. We thus provide theoretical motivation to shift trending neural-network based wave-function representations closer to state-of-the-art deep learning architectures.

https://arxiv.org/abs/1803.09780