“A Practical Introduction to Tensor Networks: Matrix Product States and
Projected Entangled Pair States”
Author: Roman Orus
Journal: Annals of Physics 349 (2014) 117-158
Notes: Friendly introduction to tensor networks for readers with a quantum physics background. Gives some overviews of basics of tensor network algorithms as well as more detailed descriptions of algorithms for one- and two-dimensional quantum systems.
“Matrix product states, DMRG, and tensor networks”
Author: Garnet Chan
Video Link 1
Video Link 2
Notes: Accessible and gradual lectures introducing core tensor network concepts and algorithms in physics and chemistry.
“Tensor Networks for Big Data Analytics and Large-Scale Optimization Problems”
Author: Andrzej Cichocki
Notes: Good introduction to tensor networks for those more familiar with applied mathematics literature and notation.
“Hand-waving and Interpretive Dance: An Introductory Course on Tensor Networks”
Authors: Jacob C. Bridgeman, Christopher T. Chubb
Journal: J. Phys. A: Math. Theor. 50 223001 (2017)
Notes: For those with a quantum information background, this is a very friendly and readable introduction to tensor networks and tensor diagram notation.
“Efficient Numerical Simulations Using Matrix-Product States”
Authors: Frank Pollmann
Link to Notes
Notes: An introduction to matrix product state methods in physics building up to Trotter gate (TEBD) algorithms for time evolution and the infinite DMRG algorithm.
“Matrix Product States and Projected Entangled Pair States: Concepts, Symmetries, and Theorems”
Authors: J. Ignacio Cira, David Perez-Garcia, Norbert Schuch, Frank Verstraete
Notes: An authoritative introduction to tensor networks from a quantum physics perspective with a rigorous discussion of concepts such as scaling of entanglement, symmetries and group theoretic properties, parent Hamiltonians, and canonical forms of tensor networks.
“Tensor Networks and Applications”
Author: Miles Stoudenmire
Description: This is a one-week course, mainly on matrix product state (MPS) tensor networks, aimed at graduate students familiar with many-body quantum mechanics.
Lecture 1 — Entanglement in quantum many-body systems.
Lecture 2 — Introduction to MPS; efficient computations with MPS; AKLT state example.
Lecture 3 — Gauges or canonical forms of MPS; matrix product operators; DMRG algorithm.
Lecture 4 — Tensor networks; scaling of entanglement for critical and 2D systems; quantum chemistry; finite temperature systems.
Lecture 5 — Introduction to machine learning; selected uses of tensor networks in machine learning.
“Two-Dimensional Tensor Networks and Contraction Algorithms”
Authors: Shi-Ju Ran, Emanuele Tirrito, Cheng Peng, Xi Chen, Luca Tagliacozzo, Gang Su, Maciej Lewenstein
Link: In: Tensor Network Contractions, Lecture Notes in Physics, vol 964. Springer
Notes: An open-access book reviewing basics of tensor networks in diagram notation, then introducing advanced techniques for tasks such as contracting two-dimensional tensor networks.
“The density-matrix renormalization group in the age of matrix product states”
Author: Ulrich Schollwoeck
Journal: Annals of Physics 326, 96 (2011)
Notes: A nearly comprehensive, and very detailed explanation of matrix product state and DMRG methods as of 2011. This article could serve as a very good introduction for those familiar with quantum mechanics and who are willing to work carefully through the many helpful steps and details offered.
“Matrix Product Operators, Matrix Product States, and ab initio Density Matrix Renormalization Group algorithms”
Authors: Garnet Kin-Lic Chan, Anna Keselman, Naoki Nakatani, Zhendong Li, Steven R. White
Notes: An extensive and readable discussion of DMRG in the matrix product state formalism as applied to Hamiltonians with long-range interactions such as in quantum chemistry. Discusses the structure of matrix product operators (MPO) in particular and gives a novel way to construct compressed MPOs for complicated Hamiltonians.
“Time-evolution methods for matrix-product states”
Author: Sebastian Paeckel, Thomas Köhler, Andreas Swoboda, Salvatore R. Manmana, Ulrich Schollwöck, Claudius Hubig
Journal: Annals of Physics 411, 167998 (2019) (open access)
Notes: Explains five current time-evolution methods for matrix-product states in extensive detail and tests them on four relevant problem settings. The content of this review is also the basis of the articles on time-evolution methods on this website.
Author: Ivan Oseledets
Journal: SIAM J. Sci. Comput., 33(5), 2295 (2011)
Notes: Very readable article introducing the idea of the tensor-train decomposition into the mathematics literature.
“Tensor Decompositions and Applications”
Author: T. G. Kolda, B. W. Bader
Journal: SIAM Review, 51(3), 455-500 (2009)
Notes: Great article introducing tensor basics and tensor decompositions.
“Matrix Product State Representations”
Authors: D. Perez-Garcia, F. Verstraete, M.M. Wolf, J.I. Cirac
Journal: Quantum Info. Comput. 7, 401–430 (2007)
Notes: One of the earlier articles proposing the idea of canonical forms of matrix product states (MPS), algorithms to compute MPS, and other properties of MPS.
“Algorithms for Entanglement Renormalization (v2)”
Author: G. Vidal
Notes: Version 2 of this article is very different from the final published version (see below). It contains lots of interesting results about optimization strategies for tensor networks, and proposals to compute layers of hierarchical tensor networks.
“Algorithms for Entanglement Renormalization”
Authors: G. Evenbly, G. Vidal
Journal: Phys. Rev. B 79, 144108
Notes: Readable article with many details about MERA tensor networks including strategies for optimizing them.
“Algorithms for Tensor Network Renormalization”
Authors: G. Evenbly
Journal: Phys. Rev. B 95, 045117
Notes: Tutorial article with details about optimization steps for isometric tensors and steps of the tensor network renormalization (TNR) algorithm.
“Finite automata for caching in matrix product algorithms”
Authors: Gregory M. Crosswhite, Dave Bacon
Journal: Phys. Rev. A 78, 012356 (2008)
Notes: Provides a nice picture and detailed understanding of how MPS and MPO tensor networks represent certain high-dimensional tensors in terms of a finite-state automaton picture.