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An augmented tree tensor network (aTTN) is a tensor network ansatz constructed by applying a layer of unitary disentanglers to a tree tensor network. The disentanglers absorb a part of the system's entanglement. This makes aTTNs suitable…

Quantum Physics · Physics 2025-07-30 Nora Reinić , Luka Pavešić , Daniel Jaschke , Simone Montangero

We show that numerical quasi-one-dimensional renormalization group allows accurate study of weakly coupled chains with modest computational effort. We perform a systematic comparison with exact diagonalization results in two and three-leg…

Strongly Correlated Electrons · Physics 2007-05-23 J. V. Alvarez , S. Moukouri

We present a quantum-inspired solver for the one-dimensional Gross-Pitaevskii equation in the Quantics Tensor-Train (QTT) representation. By evolving the system entirely within a low-rank tensor manifold, the method sidesteps the memory and…

We propose a tensor network method for investigating strongly disordered systems that is based on an adaptation of entanglement renormalization [G. Vidal, Phys. Rev. Lett. 99, 220405 (2007)]. This method makes use of the strong disorder…

Strongly Correlated Electrons · Physics 2017-10-26 Andrew M. Goldsborough , Glen Evenbly

The entanglement entropy of a subsystem $A$ of a quantum system is expressed, in the replica method, through analytic continuation with respect to n of the trace of the n-th power of the reduced density matrix $\tr\rho_A^n$. We study the…

Statistical Mechanics · Physics 2010-03-25 F. Gliozzi , L. Tagliacozzo

Quantum networks provide access to exchange of quantum information. The primary task of quantum networks is to distribute entanglement between remote nodes. Although quantum repeater protocol enables long distance entanglement distribution,…

Physics and Society · Physics 2015-03-19 Zong-Wen Wei , Bing-Hong Wang , Xiao-Pu Han

We propose an improved tensor renormalization group (TRG) algorithm, the bond-weighted TRG (BTRG). In BTRG, we generalize the conventional TRG by introducing bond weights on the edges of the tensor network. We show that BTRG outperforms the…

Statistical Mechanics · Physics 2022-03-03 Daiki Adachi , Tsuyoshi Okubo , Synge Todo

This review focuses on the field of quantum entanglement applied to condensed matter physics systems with strong correlations, a domain which has rapidly grown over the last decade. By tracing out part of the degrees of freedom of…

Strongly Correlated Electrons · Physics 2016-08-11 Nicolas Laflorencie

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

Protocols of quantum energy teleportation (QET), while retaining causality and local energy conservation, enable the transportation of energy from a subsystem of a many-body quantum system to a distant subsystem by local operations and…

Quantum Physics · Physics 2014-11-20 Masahiro Hotta

The renormalization method is specifically aimed at connecting theories describing physical processes at different length scales and thereby connecting different theories in the physical sciences. The renormalization method used today is…

Statistical Mechanics · Physics 2011-02-21 Leo P. Kadanoff

Quantum entanglement is a key resource in quantum computing and quantum information processing tasks. However, its quantification remains a major challenge since it cannot be directly extracted from physical observables. To address this…

Quantum Physics · Physics 2025-12-29 Shruti Aggarwal , Trasha Gupta , R. K. Agrawal , S. Indu

We introduce compositional tensor trains (CTTs) for the approximation of multivariate functions, a class of models obtained by composing low-rank functions in the tensor-train format. This format can encode standard approximation tools,…

Numerical Analysis · Mathematics 2025-12-23 Martin Eigel , Charles Miranda , Anthony Nouy , David Sommer

In quantum spin chains at criticality, two types of scaling for the entanglement entropy exist: one comes from conformal field theory (CFT), and the other is for entanglement support of matrix product state (MPS) approximation. They…

Statistical Mechanics · Physics 2011-09-02 Hiroaki Matsueda

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…

Quantum Physics · Physics 2009-11-13 Jose Gaite

Exact many-body quantum problems are known to be computationally hard due to the exponential scaling of the numerical resources required. Since the advent of the Density Matrix Renormalization Group, it became clear that a successful…

Quantum Physics · Physics 2012-05-21 Pietro Silvi

We propose a new tensor network renormalization group (TNR) scheme based on global optimization and introduce a new method for constructing the finite-temperature density matrix of two-dimensional quantum systems. Combining these two into a…

Strongly Correlated Electrons · Physics 2026-05-13 Atsushi Ueda , Sander De Meyer , Adwait Naravane , Victor Vanthilt , Frank Verstraete

Shared entanglement can significantly amplify classical correlations between systems interacting over a limited quantum channel. A natural avenue is to use entanglement of the same dimension as the channel because this allows for unitary…

Distributing long-distance entanglement is a fundamental goal that is necessary for a variety of tasks such as quantum communication, distributed quantum computing, and quantum metrology. Currently quantum repeater schemes typically aim to…

Quantum Physics · Physics 2024-08-02 Alexey N. Pyrkov , Ilia D. Lazarev , Tim Byrnes

Tensors with unit Frobenius norm are fundamental objects in many fields, including scientific computing and quantum physics, which are able to represent normalized eigenvectors and pure quantum states. While the tensor train decomposition…

Numerical Analysis · Mathematics 2025-11-07 Renfeng Peng , Chengkai Zhu , Bin Gao , Xin Wang , Ya-xiang Yuan
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