Work

Stack Operation of Tensor Networks

Year: 2022

Type: article

Abstract: The tensor network, as a factorization of tensors, aims at performing the operations that are common for normal tensors, such as addition, contraction, and stacking. However, because of its non-unique network structure, only the tensor network contraction is so far well defined. In this study, we propose a mathematically rigorous definition for the tensor network stack approach that compresses a large number of tensor networks into a single one without changing their structures and configurations. We illustrate the main ideas with the matrix product states based on machine learning as an example. Our results are compared with the for-loop and the efficient coding method on both CPU and GPU.

Source: Frontiers in Physics

DOI: 10.3389/fphy.2022.906399

Authors: Tianning Zhang, Tianqi Chen, Erping Li, Bo Yang, L. K. Ang

Institutions: Singapore University of Technology and Design, Nanyang Technological University, Zhejiang University

Language: English

Topic: Quantum many-body systems

Subfield: Atomic and Molecular Physics, and Optics

Field: Physics and Astronomy

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $2,490

Funders: Singapore University of Technology and Design, Office of Naval Research, Office of Naval Research Global