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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…

无序系统与神经网络 · 物理学 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…

强关联电子 · 物理学 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…

无序系统与神经网络 · 物理学 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…

强关联电子 · 物理学 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…

无序系统与神经网络 · 物理学 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…

无序系统与神经网络 · 物理学 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…

统计力学 · 物理学 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…

统计力学 · 物理学 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…

强关联电子 · 物理学 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…

凝聚态物理 · 物理学 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…

核理论 · 物理学 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…

无序系统与神经网络 · 物理学 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…

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…

强关联电子 · 物理学 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…

无序系统与神经网络 · 物理学 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…

统计力学 · 物理学 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…

无序系统与神经网络 · 物理学 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…

高能物理 - 唯象学 · 物理学 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 $\textrm{U}(1)$…

无序系统与神经网络 · 物理学 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…

统计力学 · 物理学 2024-09-20 Adam Nahum , Kay Joerg Wiese
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