Entanglement across scales: Quantics tensor trains as a natural framework for renormalization

Fuente: arXiv
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Autori principali: Rohshap, Stefan, Li, Jheng-Wei, Lorenz, Alena, Hasil, Serap, Held, Karsten, Kauch, Anna, Wallerberger, Markus
Natura: Preprint
Pubblicazione: 2025
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author Rohshap, Stefan
Li, Jheng-Wei
Lorenz, Alena
Hasil, Serap
Held, Karsten
Kauch, Anna
Wallerberger, Markus
author_facet Rohshap, Stefan
Li, Jheng-Wei
Lorenz, Alena
Hasil, Serap
Held, Karsten
Kauch, Anna
Wallerberger, Markus
contents Understanding entanglement remains one of the most intriguing problems in physics. While particle and site entanglement have been studied extensively, the investigation of length or energy scale entanglement, quantifying the information exchange between different length scales, has received far less attention. Here, we identify the quantics tensor train (QTT) technique, a matrix product state-inspired approach for overcoming computational bottlenecks in resource-intensive numerical calculations, as a renormalization group method by analytically expressing an exact cyclic reduction-based real-space renormalization scheme in QTT language, which serves as a natural formalism for the method. In doing so, we precisely match the QTT bond dimension, a measure of length scale entanglement, to the number of rescaled couplings generated in each coarse-graining renormalization step. While QTTs have so far been applied almost exclusively to numerical problems in physics, our analytical calculations demonstrate that they are also powerful tools for mitigating computational costs in semi-analytical treatments. We present our results for the one-dimensional tight-binding model with n-th-nearest-neighbor hopping, where the 2n rescaled couplings generated in the renormalization procedure precisely match the QTT bond dimension of the one-particle Green's function.
format Preprint
id arxiv_https___arxiv_org_abs_2507_19069
institution arXiv
publishDate 2025
record_format arxiv
spellingShingle Entanglement across scales: Quantics tensor trains as a natural framework for renormalization
Rohshap, Stefan
Li, Jheng-Wei
Lorenz, Alena
Hasil, Serap
Held, Karsten
Kauch, Anna
Wallerberger, Markus
Strongly Correlated Electrons
Mathematical Physics
Computational Physics
Understanding entanglement remains one of the most intriguing problems in physics. While particle and site entanglement have been studied extensively, the investigation of length or energy scale entanglement, quantifying the information exchange between different length scales, has received far less attention. Here, we identify the quantics tensor train (QTT) technique, a matrix product state-inspired approach for overcoming computational bottlenecks in resource-intensive numerical calculations, as a renormalization group method by analytically expressing an exact cyclic reduction-based real-space renormalization scheme in QTT language, which serves as a natural formalism for the method. In doing so, we precisely match the QTT bond dimension, a measure of length scale entanglement, to the number of rescaled couplings generated in each coarse-graining renormalization step. While QTTs have so far been applied almost exclusively to numerical problems in physics, our analytical calculations demonstrate that they are also powerful tools for mitigating computational costs in semi-analytical treatments. We present our results for the one-dimensional tight-binding model with n-th-nearest-neighbor hopping, where the 2n rescaled couplings generated in the renormalization procedure precisely match the QTT bond dimension of the one-particle Green's function.
title Entanglement across scales: Quantics tensor trains as a natural framework for renormalization
topic Strongly Correlated Electrons
Mathematical Physics
Computational Physics
url https://arxiv.org/abs/2507.19069