EinExprs: Contraction Paths of Tensor Networks as Symbolic Expressions

Tensor Networks are graph representations of summation expressions in which vertices represent tensors and edges represent tensor indices or vector spaces. In this work, we present EinExprs.jl, a Julia package for contraction path optimization that offers state-of-art optimizers. We propose a repres...

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Bibliographic Details
Published inarXiv.org
Main Authors Sanchez-Ramirez, Sergio, Vallès-Muns, Jofre, Garcia-Saez, Artur
Format Paper
LanguageEnglish
Published Ithaca Cornell University Library, arXiv.org 26.03.2024
Subjects
Apexes
Graph theory
Graphical representations
Greedy algorithms
Mathematical analysis
Networks
Tensors
Vector spaces
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Summary:Tensor Networks are graph representations of summation expressions in which vertices represent tensors and edges represent tensor indices or vector spaces. In this work, we present EinExprs.jl, a Julia package for contraction path optimization that offers state-of-art optimizers. We propose a representation of the contraction path of a Tensor Network based on symbolic expressions. Using this package the user may choose among a collection of different methods such as Greedy algorithms, or an approach based on the hypergraph partitioning problem. We benchmark this library with examples obtained from the simulation of Random Quantum Circuits (RQC), a well known example where Tensor Networks provide state-of-the-art methods.
ISSN:2331-8422