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
Main Authors Sanchez-Ramirez, Sergio, Vallès-Muns, Jofre, Garcia-Saez, Artur
Format Journal Article
LanguageEnglish
Published 26.03.2024
Subjects
Computer Science - Mathematical Software
Physics - Quantum Physics
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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.
DOI:10.48550/arxiv.2403.18030