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The renormalization of the effective field theories (EFTs) in many-body systems is the most pressing and challenging problem in modern nuclear ab initio calculation. For general non-relativistic EFTs, we prove that the renormalization group…

Nuclear Theory · Physics 2023-08-29 Bing-Nan Lu , Bao-Ge Deng

We introduce a tensor renormalization group scheme for coarse-graining a two-dimensional tensor network that can be successfully applied to both classical and quantum systems on and off criticality. The key innovation in our scheme is to…

Strongly Correlated Electrons · Physics 2017-03-17 Shuo Yang , Zheng-Cheng Gu , Xiao-Gang Wen

We show that the Wilsonian formulation of the renormalization group (RG) defines a quantum channel acting on the momentum-space density matrices of a quantum field theory. This information theoretical property of the RG allows us to derive…

High Energy Physics - Theory · Physics 2023-06-28 Matheus H. Martins Costa , Jeroen van den Brink , Flavio S. Nogueira , Gastão I. Krein

We study the ground state quantum phase transition by means of entanglement in the one-dimensional asymmetric Hubbard model with open boundary condition. The local entanglement between the middle two sites and the rest of the system, and…

Strongly Correlated Electrons · Physics 2009-11-13 W. L. Chan , S. J. Gu

One important step in the renormalization group (RG) approach to a lattice sandpile model is the exact enumeration of all possible toppling processes of sandpile dynamics inside a cell for RG transformations. Here we propose a computer…

Statistical Mechanics · Physics 2009-11-07 Chai-Yu Lin , Chin-Kun Hu

We perform a data-driven dimensionality reduction of the scale-dependent 4-point vertex function characterizing the functional Renormalization Group (fRG) flow for the widely studied two-dimensional $t - t'$ Hubbard model on the square…

The perturbative renormalization group(RG) equation is applied to resum divergent series of perturbative wave functions of quantum anharmonic oscillator. It is found that the resummed series gives the cumulant of the naive perturbation…

High Energy Physics - Theory · Physics 2009-09-25 Teiji Kunihiro

The density matrix renormalization group (DMRG) is a numerical method that optimizes a variational state expressed by a tensor product. We show that the ground state is not fully optimized as far as we use the standard finite system…

Statistical Mechanics · Physics 2010-05-20 H. Takasaki , T. Hikihara , T. Nishino

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

We develop a renormalization group (RG) description of the localization properties of onedimensional (1D) quasiperiodic lattice models. The RG flow is induced by increasing the unit cell of subsequent commensurate approximants. Phases of…

Disordered Systems and Neural Networks · Physics 2023-10-10 Miguel Gonçalves , Bruno Amorim , Eduardo V. Castro , Pedro Ribeiro

In this thesis we investigate the Renormalization Group (RG) approach in finite-dimensional glassy systems, whose critical features are still not well-established, or simply unknown. We focus on spin and structural-glass models built on…

Disordered Systems and Neural Networks · Physics 2015-04-02 Michele Castellana

The tensor-entanglement renormalization group approach is applied to Hamiltonians that realize a class of topologically ordered states -- string-net condensed states. We analyze phase transitions between phases with and without string-net…

Strongly Correlated Electrons · Physics 2009-03-08 Zheng-Cheng Gu , Michael Levin , Xiao-Gang Wen

We apply the real space Renormalisation Group (RNG) technique to a variety of one-dimensional Ising chains. We begin by recapitulating the work of Nauenberg for an ordered Ising chain, namely the decimation approach. We extend this work to…

Statistical Mechanics · Physics 2024-08-05 Shraddha singh , Vijay Singh

We present an elementary introduction to entanglement renormalization, a real space renormalization group for quantum lattice systems. This manuscript corresponds to a chapter of the book "Understanding Quantum Phase Transitions", edited by…

Strongly Correlated Electrons · Physics 2010-05-25 Guifre Vidal

We analyze perturbative dynamics of a composite system consisting of a quantum mechanical system and an environment by the renormalization group (RG) method. The solution obtained from the RG method has no secular terms and approximates the…

Quantum Physics · Physics 2017-10-18 Shingo Kukita

We show that artificial neural networks (ANNs) can, to high accuracy, determine the topological invariant of a disordered system given its two-dimensional real-space Hamiltonian. Furthermore, we describe a "renormalization-group" (RG)…

Disordered Systems and Neural Networks · Physics 2022-06-22 Gilad Margalit , Omri Lesser , T. Pereg-Barnea , Yuval Oreg

In this paper, we propose a modified Density Matrix Renormalization Group (DMRG) algorithm to preferentially select minimum entropy states (minimally entangled states) in finite systems with asymptotic ground state degeneracy. The algorithm…

Strongly Correlated Electrons · Physics 2013-10-01 Hong-Chen Jiang , Leon Balents

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…

Nuclear Theory · Physics 2008-11-26 S. K. Bogner , R. J. Furnstahl , R. J. Perry

We present a new ab-initio method that uses similarity renormalization group (SRG) techniques to continuously diagonalize nuclear many-body Hamiltonians. In contrast with applications of the SRG to two- and three-nucleon interactions in…

Nuclear Theory · Physics 2011-07-21 K. Tsukiyama , S. K. Bogner , A. Schwenk

Tensor renormalization group (TRG) constitutes an important methodology for accurate simulations of strongly correlated lattice models. Facilitated by the automatic differentiation technique widely used in deep learning, we propose a…

Strongly Correlated Electrons · Physics 2020-07-07 Bin-Bin Chen , Yuan Gao , Yi-Bin Guo , Yuzhi Liu , Hui-Hai Zhao , Hai-Jun Liao , Lei Wang , Tao Xiang , Wei Li , Z. Y. Xie
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