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We apply a series of projection techniques on top of tensor networks to compute energies of ground state wave functions with higher accuracy than tensor networks alone with minimal additional cost. We consider both matrix product states as…

Strongly Correlated Electrons · Physics 2014-04-10 Bryan K. Clark , Hitesh J. Changlani

We present an algorithm for the calculation of eigenstates with definite linear momentum in quantum lattices. Our method is related to the Density Matrix Renormalization Group, and makes use of the distribution of multipartite entanglement…

Strongly Correlated Electrons · Physics 2009-11-11 D. Porras , F. Verstraete , J. I. Cirac

The wave equation for vectors and symmetric tensors in spherical coordinates is studied under the divergence-free constraint. We describe a numerical method, based on the spectral decomposition of vector/tensor components onto spherical…

General Relativity and Quantum Cosmology · Physics 2009-11-23 Jerome Novak , Jean-Louis Cornou , Nicolas Vasset

We focus on an alignment-free method to estimate the underlying signal from a large number of noisy randomly shifted observations. Specifically, we estimate the mean, power spectrum, and bispectrum of the signal from the observations. Since…

Signal Processing · Electrical Eng. & Systems 2018-07-04 Hua Chen , Mona Zehni , Zhizhen Zhao

We study two-dimensional ferromagnetic Ising model on a series of regular lattices, which are represented as the tessellation of polygons with p>=5 sides, such as pentagons (p=5), hexagons (p=6), etc. Such lattices are on hyperbolic planes,…

Statistical Mechanics · Physics 2008-03-31 Roman Krcmar , Andrej Gendiar , Kouji Ueda , Tomotoshi Nishino

We introduce a coarse-graining transformation for tensor networks that can be applied to study both the partition function of a classical statistical system and the Euclidean path integral of a quantum many-body system. The scheme is based…

Strongly Correlated Electrons · Physics 2015-11-04 Glen Evenbly , Guifre Vidal

We present a method for contracting a square-lattice tensor network in two dimensions, based on auxiliary tensors accomplishing successive truncations (renormalization) of 8-index tensors for 2 by 2 plaquettes into 4-index tensors. The…

Strongly Correlated Electrons · Physics 2011-05-10 Ling Wang , Ying-Jer Kao , Anders W. Sandvik

Time evolution and scattering simulation in phenomenological models are of great interest for testing and validating the potential for near-term quantum computers to simulate quantum field theories. Here, we simulate one-particle…

Quantum Physics · Physics 2021-05-26 Kübra Yeter-Aydeniz , George Siopsis , Raphael C. Pooser

In this paper, we introduce new reference observables to establish a scaling formula in the renormalization group equation. Using the transfer matrix method, we calculate the two point observables of the one dimensional Ising model without…

Probability · Mathematics 2024-05-14 Cui Kaiyuan , Gong Fuzhou

The density matrix renormalization group method is generalized to one dimensional random systems. Using this method, the energy gap distribution of the spin-1/2 random antiferromagnetic Heisenberg chain is calculated. The results are…

Condensed Matter · Physics 2009-10-28 Kazuo Hida

The density matrix renormalization group method is applied to obtain the ground state phase diagram of the single impurity Anderson model on the honeycomb lattice at half filling. The calculation of local static quantities shows that the…

Strongly Correlated Electrons · Physics 2016-06-23 Tomonori Shirakawa , Seiji Yunoki

Linear optical circuits of growing complexity are playing an increasing role in emerging photonic quantum technologies. Individual photonic devices are typically described by a unitary matrix containing amplitude and phase information, the…

Quantum Physics · Physics 2012-08-15 Anthony Laing , Jeremy L. O'Brien

We consider one dimensional quantum Ising spin-1/2 chains with two-valued nearest neighbor couplings arranged in a quasi-periodic sequence, with uniform, transverse magnetic field. By employing the Jordan-Wigner transformation of the spin…

Mathematical Physics · Physics 2013-04-11 W. N. Yessen

We develop a new methodology to contract tensor networks within the corner transfer matrix renormalization group approach for a wide range of two-dimensional lattice geometries. We discuss contraction algorithms on the example of…

Statistical Mechanics · Physics 2024-04-19 I. V. Lukin , A. G. Sotnikov

We construct a tensor network representation of the partition function for the massless Schwinger model on a two dimensional lattice using staggered fermions. The tensor network representation allows us to include a topological term. Using…

High Energy Physics - Lattice · Physics 2020-06-24 Nouman Butt , Simon Catterall , Yannick Meurice , Judah Unmuth-Yockey

The fields of entanglement theory and tensor networks have recently emerged as central tools for characterising quantum phases of matter. In this article, we determine the entanglement structure of ground states of gapped symmetric quantum…

Quantum Physics · Physics 2025-10-16 Laurens Lootens , Clement Delcamp , Frank Verstraete

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 report a real-space renormalization group (RSRG) algorithm, which is formulated through Baxter's corner transfer matrix (CTM), for two-dimensional (d = 2) classical lattice models. The new method performs the renormalization group…

Statistical Mechanics · Physics 2008-02-03 Tomotoshi Nishino , Kouichi Okunishi

We present a numerical method for calculating piecewise smooth spectral functions of correlated quantum systems in the thermodynamic limit from the spectra of finite systems computed using the dynamical or correction-vector density-matrix…

Strongly Correlated Electrons · Physics 2014-05-05 Martin Paech , Eric Jeckelmann

We present a novel approach to the regression of quantum mechanical energies based on a scattering transform of an intermediate electron density representation. A scattering transform is a deep convolution network computed with a cascade of…

Machine Learning · Computer Science 2016-05-23 Matthew Hirn , Nicolas Poilvert , Stéphane Mallat