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We train machine learning algorithms to infer the entanglement structure of disordered long-range interacting quantum spin chains by learning from the strong disorder renormalisation group (SDRG) method. The system consists of…

Disordered Systems and Neural Networks · Physics 2026-03-06 A. Ustyuzhanin , J. Vahedi , S. Kettemann

The interplay of disorder and interactions is a challenging topic of condensed matter physics, where correlations are crucial and exotic phases develop. In one spatial dimension, a particularly successful method to analyze such problems is…

Strongly Correlated Electrons · Physics 2019-12-10 V. L. Quito , Pedro L. S. Lopes , José A. Hoyos , E. Miranda

Strong Disorder Renormalization is an energy-based renormalization that leads to a complicated renormalized topology for the surviving clusters as soon as $d>1$. In this paper, we propose to include Strong Disorder Renormalization ideas…

Disordered Systems and Neural Networks · Physics 2012-05-04 Cecile Monthus , Thomas Garel

We implement an efficient strong-disorder renormalization-group (SDRG) procedure to study disordered tight-binding models in any dimension and on the Erdos-Renyi random graphs, which represent an appropriate infinite dimensional limit. Our…

Strongly Correlated Electrons · Physics 2017-08-02 Hossein Javan Mard , Jose A. Hoyos , Eduardo Miranda , Vladimir Dobrosavljevic

Percolation theory dictates an intuitive picture depicting correlated regions in complex systems as densely connected clusters. While this picture might be adequate at small scales and apart from criticality, we show that highly correlated…

Disordered Systems and Neural Networks · Physics 2020-12-17 István A. Kovács , Róbert Juhász

Exploring and understanding topological phases in systems with strong distributed disorder requires developing fundamentally new approaches to replace traditional tools such as topological band theory. Here, we present a general real-space…

Disordered Systems and Neural Networks · Physics 2024-04-25 Zhe Zhang , Yifei Guan , Junda Wang , Benjamin Apffel , Aleksi Bossart , Haoye Qin , Oleg V. Yazyev , Romain Fleury

We study the local persistence probability during non-stationary time evolutions in disordered contact processes with long-range interactions by a combination of the strong-disorder renormalization group (SDRG) method, a phenomenological…

Statistical Mechanics · Physics 2020-08-19 Róbert Juhász

We study the time-dependence of the local persistence probability during a non-stationary time evolution in the disordered contact process in $d=1,2$, and $3$ dimensions. We present a method for calculating the persistence with the…

Statistical Mechanics · Physics 2020-07-03 Róbert Juhász , István A. Kovács

We propose a simple modification of the density matrix renormalization group (DMRG) method in order to tackle strongly disordered quantum spin chains. Our proposal, akin to the idea of the adaptive time-dependent DMRG, enables us to reach…

Strongly Correlated Electrons · Physics 2018-11-14 J. C. Xavier , J. A. Hoyos , E. Miranda

We present a detailed discussion of a novel dynamical renormalization group scheme: the Dynamically Driven Renormalization Group (DDRG). This is a general renormalization method developed for dynamical systems with non-equilibrium critical…

Condensed Matter · Physics 2009-10-28 Alessandro Vespignani , Stefano Zapperi , Vittorio Loreto

The Similarity Renormalization Group (SRG) is a continuous series of unitary transformations that can be implemented as a flow equation. When the relative kinetic energy ($\Trel$) is used in the SRG generator, nuclear structure calculations…

Nuclear Theory · Physics 2011-03-22 K. A. Wendt , R. J. Furnstahl , R. J. Perry

The density matrix renormalization group (DMRG) has been extended to study quantum phase transitions on random graphs of fixed connectivity. As a relevant example, we have analysed the random Ising model in a transverse field. If the…

Disordered Systems and Neural Networks · Physics 2009-11-11 Javier Rodriguez-Laguna

We propose a cross-order Laplacian renormalization group (X-LRG) scheme for arbitrary higher-order networks. The renormalization group is a pillar of the theory of scaling, scale-invariance, and universality in physics. An RG scheme based…

Statistical Mechanics · Physics 2024-02-07 Marco Nurisso , Marta Morandini , Maxime Lucas , Francesco Vaccarino , Tommaso Gili , Giovanni Petri

The Kato-Bloch perturbation formalism is used to present a density-matrix renormalization-group (DMRG) method for strongly anisotropic two-dimensional systems. This method is used to study Heisenberg chains weakly coupled by the transverse…

Strongly Correlated Electrons · Physics 2009-11-10 S. Moukouri

We show that an interaction decaying as a stretched exponential function of the distance, $J(l)\sim e^{-cl^a}$, is able to alter the universality class of short-range systems having an infinite-disorder critical point. To do so, we study…

Disordered Systems and Neural Networks · Physics 2015-06-19 Róbert Juhász

The density matrix renormalization group (DMRG) is applied to some one-dimensional reaction-diffusion models in the vicinity of and at their critical point. The stochastic time evolution for these models is given in terms of a non-symmetric…

Statistical Mechanics · Physics 2011-10-11 Enrico Carlon , Malte Henkel , Ulrich Schollwoeck

For the DNA denaturation transition in the presence of random contact energies, or equivalently the disordered wetting transition, we introduce a Strong Disorder Renewal Approach to construct the optimal contacts in each disordered sample…

Disordered Systems and Neural Networks · Physics 2017-01-23 Cecile Monthus

Dense relativistic matter has attracted a lot of attention over many decades now, with a focus on an understanding of the phase structure and thermodynamics of dense strong-interaction matter. The analysis of dense strong-interaction matter…

High Energy Physics - Phenomenology · Physics 2021-11-17 Jens Braun , Timon Dörnfeld , Benedikt Schallmo , Sebastian Töpfel

We develop an excited-state real-space renormalization group (RSRG-X) formalism to describe the dynamics of conserved densities in randomly interacting spin-$\frac{1}{2}$ systems. Our formalism is suitable for systems with $\textrm{U}(1)$…

Disordered Systems and Neural Networks · Physics 2025-08-18 Yi J. Zhao , Samuel J. Garratt , Joel E. Moore

We analyze the renormalization-group (RG) flows of two effective Lagrangians, one for measurement induced transitions of monitored quantum systems and one for entanglement transitions in random tensor networks. These Lagrangians, previously…

Statistical Mechanics · Physics 2024-09-20 Adam Nahum , Kay Joerg Wiese
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