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We discuss in detail algorithms for implementing tensor network renormalization (TNR) for the study of classical statistical and quantum many-body systems. Firstly, we recall established techniques for how the partition function of a 2D…

Strongly Correlated Electrons · Physics 2017-01-18 Glen Evenbly

A recently introduced Renormalization Group approach to frustrated spin models is applied in three dimensions through Monte Carlo computations. A class of spin glass models is analysed, with correlated disorder variables given by a Z_2…

Statistical Mechanics · Physics 2007-05-23 Leonardo Gnesi , Roberto Petronzio , Francesco Rosati

The phase diagram of a novel two-dimensional frustrated Ising model with both anti-ferromagnetic and ferromagnetic couplings is studied using Tensor-Network Renormalization-Group techniques. This model can be seen as two anti-ferromagnetic…

Statistical Mechanics · Physics 2025-02-19 Christophe Chatelain

We introduce a variational manifold of simple tensor network states for the study of a family of constrained models that describe spin-1/2 systems as realized by Rydberg atom arrays. Our manifold permits analytical calculation via…

Quantum Physics · Physics 2024-05-13 Joey Li , Giuliano Giudici , Hannes Pichler

In this paper we explore the practical use of the corner transfer matrix and its higher-dimensional generalization, the corner tensor, to develop tensor network algorithms for the classical simulation of quantum lattice systems of infinite…

Strongly Correlated Electrons · Physics 2012-05-11 Roman Orus

Based on exact diagonalization data for finite quantum Heisenberg antiferromagnets on two frustrated lattices (two-leg ladder and bilayer) and analytical arguments we map low-energy degrees of freedom of the spin models in a magnetic field…

Strongly Correlated Electrons · Physics 2010-12-22 Oleg Derzhko , Taras Krokhmalskii , Johannes Richter

We study topology of frustration in $d$-dimensional Ising spin glasses with $d\ge 2$ with nearest-neighbor interactions. We prove the following: For any given spin configuration, the domain walls on the unfrustration network are all…

Mathematical Physics · Physics 2010-04-08 Tohru Koma

We review recent results, obtained with P. Fendley, on frustration of quantum charges in lattice models for itinerant fermions with strong repulsive interactions. A judicious tuning of kinetic and interaction terms leads to models…

Strongly Correlated Electrons · Physics 2009-11-13 Liza Huijse , Kareljan Schoutens

We have developed an efficient tensor network algorithm for spin ladders, which generates ground-state wave functions for infinite-size quantum spin ladders. The algorithm is able to efficiently compute the ground-state fidelity per lattice…

Statistical Mechanics · Physics 2011-05-17 Sheng-Hao Li , Yao-Heng Su , Yan-Wei Dai , Huan-Qiang Zhou

The existence of definite orders in frustrated quantum systems is related rigorously to the occurrence of fully factorized ground states below a threshold value of the frustration. Ground-state separability thus provides a natural measure…

Statistical Mechanics · Physics 2010-05-21 Salvatore M. Giampaolo , Gerardo Adesso , Fabrizio Illuminati

Two-dimensional (2D) quantum magnetism is a paradigm in strongly correlated many-body physics. The understanding of 2D quantum magnetism can be expedited by employing a controllable quantum simulator that faithfully maps 2D-spin…

Topological frustration arises when boundary conditions impose geometric frustration in a quantum system, creating delocalized defects in the ground states and profoundly altering the low-energy properties. While previous studies have been…

It is by now well-known that ground states of gapped one-dimensional (1d) quantum-many body systems with short-range interactions can be studied efficiently using classical computers and matrix product state techniques. A corresponding…

Quantum Physics · Physics 2017-08-31 Thomas Barthel

Tensor network decompositions offer an efficient description of certain many-body states of a lattice system and are the basis of a wealth of numerical simulation algorithms. In a recent paper [arXiv:0907.2994v1] we discussed how to…

Strongly Correlated Electrons · Physics 2011-06-01 Sukhwinder Singh , Robert N. C. Pfeifer , Guifre Vidal

We introduce a method based on semidefinite programming that produces rigorous two-sided bounds on ground state energy densities and correlation functions of translation-invariant classical spin models on infinite lattices. In this method,…

Statistical Mechanics · Physics 2026-05-11 Nisarga Paul , Gil Refael

Most equivariant neural networks rely on a single global symmetry, limiting their use in domains where symmetries are instead local. We introduce Torsor CNNs, a framework for learning on graphs with local symmetries encoded as edge…

Machine Learning · Computer Science 2025-10-28 Daiyuan Li , Shreya Arya , Robert Ghrist

Three 2D spin models made of frustrated zig-zag chains with competing interactions which by exact summation with respect to some degrees of freedom can be replaced by an effective temperature-dependent interaction were considered. The first…

Statistical Mechanics · Physics 2024-03-28 Jozef Sznajd

In order to clarify a crucial role of orbital degree of freedom in geometrically frustrated systems, we investigate both ground- and excited-state properties of the e_g-orbital degenerate Hubbard model on two kinds of lattices, ladder and…

Strongly Correlated Electrons · Physics 2009-11-10 Hiroaki Onishi , Takashi Hotta

Kinetic magnetism is an iconic and rare example of collective quantum order that emerges from the interference of paths taken by a hole in a sea of strongly interacting fermions. Here the lattice topology plays a fundamental role, with odd…

Strongly Correlated Electrons · Physics 2026-03-17 Revathy B S , Shovan Dutta

Quantum computers are expected to enable fast solving of large-scale combinatorial optimization problems. However, their limitations in fidelity and the number of qubits prevent them from handling real-world problems. Recently, a…

Statistical Mechanics · Physics 2025-07-23 Hyakka Nakada , Kotaro Tanahashi , Shu Tanaka