GuiTeNet: A Graphical User Interface for Tensor Networks

Lisa Sahlmann; Christian B. Mendl

doi:10.5334/jors.304

GuiTeNet: A Graphical User Interface for Tensor Networks

Lisa Sahlmann, Christian B. Mendl

Informatik 5 - Professur für Quantum Computing

Technische Universität Dresden

Publikation: Beitrag in Fachzeitschrift › Artikel › Begutachtung

Abstract

We introduce a graphical user interface for constructing arbitrary tensor networks and specifying common operations like contractions or splitting, denoted GuiTeNet. Tensors are represented as nodes with attached legs, corresponding to the ordered dimensions of the tensor. GuiTeNet visualizes the current network, and instantly generates Python/NumPy source code for the hitherto sequence of user actions. Support for additional programming languages is planned for the future. We discuss the elementary operations on tensor networks used by GuiTeNet, together with high-level optimization strategies. The software runs directly in web browsers and is available online at guitenet.org.

Originalsprache	Englisch
Seiten (von - bis)	1-6
Seitenumfang	6
Fachzeitschrift	Journal of Open Research Software
Jahrgang	8
DOIs	https://doi.org/10.5334/jors.304
Publikationsstatus	Veröffentlicht - 2020

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abstract = "We introduce a graphical user interface for constructing arbitrary tensor networks and specifying common operations like contractions or splitting, denoted GuiTeNet. Tensors are represented as nodes with attached legs, corresponding to the ordered dimensions of the tensor. GuiTeNet visualizes the current network, and instantly generates Python/NumPy source code for the hitherto sequence of user actions. Support for additional programming languages is planned for the future. We discuss the elementary operations on tensor networks used by GuiTeNet, together with high-level optimization strategies. The software runs directly in web browsers and is available online at guitenet.org.",

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author = "Lisa Sahlmann and Mendl, {Christian B.}",

note = "Publisher Copyright: {\textcopyright} 2020. The Author(s). This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International License (CC-BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. See http://creativecommons.org/licenses/by/4.0/.",

year = "2020",

doi = "10.5334/jors.304",

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AU - Sahlmann, Lisa

AU - Mendl, Christian B.

N1 - Publisher Copyright: © 2020. The Author(s). This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International License (CC-BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. See http://creativecommons.org/licenses/by/4.0/.

PY - 2020

Y1 - 2020

N2 - We introduce a graphical user interface for constructing arbitrary tensor networks and specifying common operations like contractions or splitting, denoted GuiTeNet. Tensors are represented as nodes with attached legs, corresponding to the ordered dimensions of the tensor. GuiTeNet visualizes the current network, and instantly generates Python/NumPy source code for the hitherto sequence of user actions. Support for additional programming languages is planned for the future. We discuss the elementary operations on tensor networks used by GuiTeNet, together with high-level optimization strategies. The software runs directly in web browsers and is available online at guitenet.org.

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GuiTeNet: A Graphical User Interface for Tensor Networks

Abstract

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