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By using the density matrix renormalization group (DMRG) technique, the incommensurate quantum Frenkel-Kontorova model is investigated numerically. It is found that when the quantum fluctuation is strong enough, the \emph{g}-function…

其他凝聚态物理 · 物理学 2009-11-11 B. Hu , J. X. Wang

The numerical study of anyonic systems is known to be highly challenging due to their non-bosonic, non-fermionic particle exchange statistics, and with the exception of certain models for which analytical solutions exist, very little is…

强关联电子 · 物理学 2015-12-25 Robert N. C. Pfeifer , Sukhwinder Singh

\textbf{Background} The reach of \textit{ab initio} theory has greatly increased in recent decades. However, predicting the location of the drip lines remains challenging due to uncertainties in nuclear forces and difficulties in describing…

核理论 · 物理学 2026-01-23 A. Sehovic , K. Fossez , H. Hergert

The renormalization group is a tool that allows one to obtain a reduced description of systems with many degrees of freedom while preserving the relevant features. In the case of quantum systems, in particular, one-dimensional systems…

量子物理 · 物理学 2009-11-13 Jose Gaite

We introduce the density matrix renormalization group (DMRG) method as an efficient computational tool for one-exciton approximations with off-diagonal disorder. This method allows us to reduce the computational effort by targetting only a…

强关联电子 · 物理学 2007-05-23 M. A. Martin-Delgado , J. Rodriguez-Laguna , G. Sierra

Nematic order is an exotic property observed in several strongly correlated systems, such as the iron-based superconductors. Using large-scale density matrix renormalization group (DMRG) techniques, we study at zero-temperature the nematic…

强关联电子 · 物理学 2020-01-16 Wen-Jun Hu , Shou-Shu Gong , Hsin-Hua Lai , Qimiao Si , Elbio Dagotto

The similarity renormalization group (SRG) is based on unitary transformations that suppress off-diagonal matrix elements, forcing the hamiltonian towards a band-diagonal form. A simple SRG transformation applied to nucleon-nucleon…

核理论 · 物理学 2008-11-26 S. K. Bogner , R. J. Furnstahl , R. J. Perry

We extend the symmetrized density matrix renormalization group (SDMRG) method to compute the dynamic nonlinear optic coefficients for long chains. By computing correction vectors in the appropriate symmetry subspace we obtain the dynamic…

凝聚态物理 · 物理学 2007-05-23 Swapan K. Pati , S. Ramasesha , Z. Shuai , J. L. Bredas

We present a tree-tensor-network-based method to study strongly correlated systems with nonlocal interactions in higher dimensions. Although the momentum-space and quantum-chemistry versions of the density matrix renormalization group…

强关联电子 · 物理学 2010-11-08 Valentin Murg , Örs Legeza , Reinhard M. Noack , Frank Verstraete

In the last decade, the quantum chemical version of the density matrix renormalization group (DMRG) method has established itself as the method of choice for calculations of strongly correlated molecular systems. Despite its favourable…

化学物理 · 物理学 2016-11-15 Libor Veis , Andrej Antalík , Jiří Brabec , Frank Neese , Örs Legeza , Jiří Pittner

The one dimensional Hubbard model with nearest and (negative) next-nearest neighbour hopping has been studied with the density-matrix renormalization group (DMRG) method. A large region of ferromagnetism has been found for finite density…

强关联电子 · 物理学 2009-10-28 S. Daul , R. Noack

We introduce a versatile and practical framework for applying matrix product state techniques to continuous quantum systems. We divide space into multiple segments and generate continuous basis functions for the many-body state in each…

量子气体 · 物理学 2022-06-09 Shovan Dutta , Anton Buyskikh , Andrew J. Daley , Erich J. Mueller

We introduce the transcorrelated Density Matrix Renormalization Group (tcDMRG) theory for the efficient approximation of the energy for strongly correlated systems. tcDMRG encodes the wave function as a product of a fixed Jastrow or…

强关联电子 · 物理学 2020-11-13 Alberto Baiardi , Markus Reiher

Density matrix renormalization group (DMRG) is one of the most powerful numerical methods available for many-body systems. In this work, we develop a perturbation theory of DMRG (PT-DMRG) to largely increase its accuracy in an extremely…

强关联电子 · 物理学 2017-03-01 Emanuele Tirrito , Shi-Ju Ran , Andrew J. Ferris , Ian P. McCulloch , Maciej Lewenstein

Exact diagonalization is a powerful tool to study fractional quantum Hall (FQH) systems. However, its capability is limited by the exponentially increasing computational cost. In order to overcome this difficulty,…

强关联电子 · 物理学 2015-10-01 Zhao Liu , R. N. Bhatt

The equilibrium transport properties of an elementary nanostructured device with side-coupled geometry are computed and related to universal functions. The computation relies on a real-space formulation of the numerical…

强关联电子 · 物理学 2021-09-28 Ana Luiza Ferrari , Luiz N. Oliveira

We present the real-time renormalization group (RTRG) method as a method to describe the stationary state current through generic multi-level quantum dots with a complex setup in nonequilibrium. The employed approach consists of a very…

强关联电子 · 物理学 2019-05-28 Carsten J. Lindner , Fabian B. Kugler , Volker Meden , Herbert Schoeller

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 apply the adaptive time-dependent Density Matrix Renormalization Group method (tDMRG) to the study of transport properties of quantum-dot systems connected to metallic leads. Finite-size effects make the usual tDMRG description of the…

介观与纳米尺度物理 · 物理学 2008-12-03 Luis G. G. V. Dias da Silva , F. Heidrich-Meisner , A. E. Feiguin , C. A. Busser , G. B. Martins , E. V. Anda , E. Dagotto

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
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