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Title: Stack operation of tensor networks
Authors: Zhang, Tianning
Chen, Tianqi
Li, Erping
Yang, Bo
Ang, L. K.
Keywords: Science::Physics
Issue Date: 2022
Source: Zhang, T., Chen, T., Li, E., Yang, B. & Ang, L. K. (2022). Stack operation of tensor networks. Frontiers in Physics, 10, 906399-.
Journal: Frontiers in Physics
Abstract: The tensor network, as a facterization of tensors, aims at performing the operations that are common for normal tensors, such as addition, contraction and stacking. However, due to its non-unique network structure, only the tensor network contraction is so far well defined. In this paper, we propose a mathematically rigorous definition for the tensor network stack approach, that compress a large amount of tensor networks into a single one without changing their structures and configurations. We illustrate the main ideas with the matrix product states based machine learning as an example. Our results are compared with the for loop and the efficient coding method on both CPU and GPU.
ISSN: 2296-424X
DOI: 10.3389/fphy.2022.906399
Schools: School of Physical and Mathematical Sciences 
Rights: © 2022 Zhang, Chen, Li, Yang and Ang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
Fulltext Permission: open
Fulltext Availability: With Fulltext
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