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The ITensor Software Library for Tensor Network Calculations

TLDR
The philosophy behind ITensor, a system for programming tensor network calculations with an interface modeled on tensor diagram notation, and examples of each part of the interface including Index objects, the ITensor product operator, Tensor factorizations, tensor storage types, algorithms for matrix product state (MPS) and matrix product operator (MPO) tensor networks, and the NDTensors library are discussed.
Abstract
ITensor is a system for programming tensor network calculations with an interface modeled on tensor diagram notation, which allows users to focus on the connectivity of a tensor network without manually bookkeeping tensor indices. The ITensor interface rules out common programming errors and enables rapid prototyping of tensor network algorithms. After discussing the philosophy behind the ITensor approach, we show examples of each part of the interface including Index objects, the ITensor product operator, tensor factorizations, tensor storage types, algorithms for matrix product state (MPS) and matrix product operator (MPO) tensor networks, quantum number conserving block-sparse tensors, and the NDTensors library. We also review publications that have used ITensor for quantum many-body physics and for other areas where tensor networks are increasingly applied. To conclude we discuss promising features and optimizations to be added in the future.

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Finite-temperature transport in one-dimensional quantum lattice models

TL;DR: In this paper, a review of the current understanding of transport in one-dimensional lattice models, in particular in the paradigmatic example of the spin-1/2 XXZ and Fermi-Hubbard models, is reviewed, as well as state-of-theart theoretical methods, including both analytical and computational approaches.
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Quantum phases of Rydberg atoms on a kagome lattice.

TL;DR: This work theoretically investigate the quantum phases that can be realized by arranging such Rydberg atoms on a kagome lattice, and identifies an intriguing regime that constitutes a promising candidate for hosting a phase with long-range quantum entanglement and topological order.
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Provably efficient machine learning for quantum many-body problems.

TL;DR: It is proved that classical ML algorithms can efficiently predict ground state properties of gapped Hamiltonian in finite spatial dimensions, after learning from data obtained by measuring other Hamiltonians in the same quantum phase of matter.
References
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Density matrix formulation for quantum renormalization groups

TL;DR: A generalization of the numerical renormalization-group procedure used first by Wilson for the Kondo problem is presented and it is shown that this formulation is optimal in a certain sense.
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The density-matrix renormalization group in the age of matrix product states

TL;DR: This paper gives a detailed exposition of current DMRG thinking in the MPS language in order to make the advisable implementation of the family of D MRG algorithms in exclusively MPS terms transparent.
Journal ArticleDOI

The density-matrix renormalization group in the age of matrix product states

TL;DR: The density matrix renormalization group method (DMRG) has established itself over the last decade as the leading method for the simulation of the statics and dynamics of one-dimensional strongly correlated quantum lattice systems as mentioned in this paper.
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