Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Log inSign up
Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Blog
Job Board
Q3AS

© 2026 Aqora Quantum S.A.S.

TermsPrivacyLegal Notice

arXiv:2106.03854 · 中国科学通报:英文版

Locally accurate matrix product approximation to thermal states

Yichen Huang

Published Aug 19, 2021• 5 citations
arXivHTMLPDF
DOI
10.1016/j.scib.2021.08.011

Related papers

Rényi free energy and variational approximations to thermal states

Giacomo Giudice, Aslı Çakan, J. I. Cirac +1 more

We propose the construction of thermodynamic ensembles that minimize the R\'enyi free energy, as an alternative to Gibbs states. For large systems, the local properties of these R\'enyi ensembles coincide with those of thermal equilibrium and they can be used as approximations to thermal states. We provide algorithms to find tensor network approximations to the 2-R\'enyi ensemble. In particular, a matrix-product-state representation can be found by using gradient-based optimization on Riemannian manifolds or via a nonlinear evolution which yields the desired state as a fixed point. We analyze the performance of the algorithms and the properties of the ensembles on one-dimensional spin chains.

arXiv:2012.12848•Physical review. B./Physical review. B•May 17, 2021•8 citations

Post Matrix Product State Methods: from low-energy dynamics to thermalization

Jutho Haegeman, Laurens Vanderstraeten, Frank Verstraete

Bulletin of the American Physical Society•Mar 7, 2018•0 citations

Matrix product state approximations: Bringing theory closer to practice

Yichen Huang

•Nov 6, 2019•8 citations

Locally accurate matrix product state approximations with constant bond dimension for ground states of gapped 1D models

Alexander M. Dalzell, Fernando G. S. L. Brandão

Bulletin of the American Physical Society•Mar 5, 2019•0 citations

Probing the Dynamical Structure Factor of Quantum Spin Chains via Low-Temperature Gibbs States with Matrix Product State Subspace Expansion

Tomoya Takahashi, Wei-Lin Tu, Ji-Yao Chen +1 more

Studying finite-temperature properties with tensor networks is notoriously difficult, especially at low temperatures, due to the rapid growth of entanglement and the complexity of thermal states. Existing methods like purification and minimally entangled typical thermal states offer partial solutions but struggle with scalability and accuracy in low-temperature regime. To overcome these limitations, we propose a new approach based on generating-function matrix product states (GFMPS). By directly computing a large set of Bloch-type excited states, we construct Gibbs states that moderate the area-law constraint, enabling accurate and efficient approximation of low-temperature thermal behavior. Our benchmark results show magnificent agreement with both exact diagonalization and experimental observations, validating the accuracy of our approach. This method offers a promising new direction for overcoming the longstanding challenges of studying low-temperature properties within the tensor network framework. We also expect that our method will facilitate the numerical simulation of quantum materials in comparison with experimental observations.

arXiv:2601.09326•Jan 14, 2026•0 citations

Matrix Product Representation of Thermal Pure Quantum States

Atsushi Iwaki, Shimizu Akira, Chisa Hotta

日本物理学会講演概要集•Jan 1, 2020•0 citations
We propose the construction of thermodynamic ensembles that minimize the R\'enyi free energy, as an alternative to Gibbs states. For large systems, the local properties of these R\'enyi ensembles coincide with those of thermal equilibrium and they can be used as approximations to thermal states. We provide algorithms to find tensor network approximations to the 2-R\'enyi ensemble. In particular, a matrix-product-state representation can be found by using gradient-based optimization on Riemannian manifolds or via a nonlinear evolution which yields the desired state as a fixed point. We analyze the performance of the algorithms and the properties of the ensembles on one-dimensional spin chains.
Studying finite-temperature properties with tensor networks is notoriously difficult, especially at low temperatures, due to the rapid growth of entanglement and the complexity of thermal states. Existing methods like purification and minimally entangled typical thermal states offer partial solutions but struggle with scalability and accuracy in low-temperature regime. To overcome these limitations, we propose a new approach based on generating-function matrix product states (GFMPS). By directly computing a large set of Bloch-type excited states, we construct Gibbs states that moderate the area-law constraint, enabling accurate and efficient approximation of low-temperature thermal behavior. Our benchmark results show magnificent agreement with both exact diagonalization and experimental observations, validating the accuracy of our approach. This method offers a promising new direction for overcoming the longstanding challenges of studying low-temperature properties within the tensor network framework. We also expect that our method will facilitate the numerical simulation of quantum materials in comparison with experimental observations.