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Correlator product states (CPS) are a powerful and very broad class of states for quantum lattice systems whose amplitudes can be sampled exactly and efficiently. They work by gluing together states of overlapping clusters of sites on the…

Quantum Physics · Physics 2018-03-16 Stephen R. Clark

We show that general string-net condensed states have a natural representation in terms of tensor product states (TPS) . These TPS's are built from local tensors. They can describe both states with short-range entanglement (such as the…

Strongly Correlated Electrons · Physics 2009-11-13 Zheng-Cheng Gu , Michael Levin , Brian Swingle , Xiao-Gang Wen

Recent work by Wu {\em et al.} [arXiv:1910.11011] proposed a numerical method, so-called matrix product operator-matrix product state (MPO-MPS) method, by which several types of quantum many-body wave functions, in particular, the projected…

Strongly Correlated Electrons · Physics 2020-04-29 Hui-Ke Jin , Hong-Hao Tu , Yi Zhou

We present a new wavefunction ansatz that combines the strengths of spin projection with the language of matrix product states (MPS) and matrix product operators (MPO) as used in the density matrix renormalization group (DMRG).…

Chemical Physics · Physics 2017-05-05 Zhendong Li , Garnet Kin-Lic Chan

Neural network quantum states emerge as a promising tool for solving quantum many-body problems. However, its successes and limitations are still not well-understood in particular for Fermions with complex sign structures. Based on our…

Quantum Physics · Physics 2025-11-10 Zhendong Li , Tong Zhao , Bohan Zhang

Efficient methods to access the entanglement of a quantum many-body state, where the complexity generally scales exponentially with the system size $N$, have long a concern. Here we propose the Schmidt tensor network state (Schmidt TNS)…

Quantum Physics · Physics 2023-07-18 Peng-Fei Zhou , Ying Lu , Jia-Hao Wang , Shi-Ju Ran

We classify one-dimensional symmetry-protected topological (SPT) phases protected by dipole symmetries. A dipole symmetry comprises two sets of symmetry generators: charge and dipole operators, which together form a non-trivial algebra with…

Strongly Correlated Electrons · Physics 2023-11-10 Ho Tat Lam

Quantum machine learning (QML) is a rapidly expanding field that merges the principles of quantum computing with the techniques of machine learning. One of the powerful mathematical frameworks in this domain is tensor networks. These…

Quantum Physics · Physics 2025-05-27 Alex Mossi , Bojan Žunkovic , Kyriakos Flouris

Tensor network methods have proved to be highly effective in addressing a wide variety of physical scenarios, including those lacking an intrinsic one-dimensional geometry. In such contexts, it is possible for the problem to exhibit a weak…

In stochastic modeling, there has been a significant effort towards finding predictive models that predict a stochastic process' future using minimal information from its past. Meanwhile, in condensed matter physics, matrix product states…

Quantum Physics · Physics 2019-02-05 Chengran Yang , Felix C. Binder , Varun Narasimhachar , Mile Gu

This work is devoted to the study Translation-Invariant (TI) Matrix Product State (MPS) representations of quantum states with periodic boundary conditions (PBC). We pursue two directions: we introduce new methods for constructing TI MPS…

Quantum Physics · Physics 2023-09-01 Petr Klimov , Richik Sengupta , Jacob Biamonte

Symmetry-protected topological (SPT) phases exhibit nontrivial order if symmetry is respected but are adiabatically connected to the trivial product phase if symmetry is not respected. However, unlike the symmetry-breaking phase, there is…

Strongly Correlated Electrons · Physics 2016-05-04 Ching-Yu Huang , Tzu-Chieh Wei

Tensor product state (TPS) based methods are powerful tools to efficiently simulate quantum many-body systems in and out of equilibrium. In particular, the one-dimensional matrix-product (MPS) formalism is by now an established tool in…

Strongly Correlated Electrons · Physics 2018-12-03 Johannes Hauschild , Frank Pollmann

In this work, we investigate the universal representation capacity of the Matrix Product States (MPS) from the perspective of boolean functions and continuous functions. We show that MPS can accurately realize arbitrary boolean functions by…

Machine Learning · Statistics 2025-10-16 Erdong Guo , David Draper

Tensor networks are an efficient platform to represent interesting quantum states of matter as well as to compute physical observables and information-theoretic quantities. We present a general protocol to construct fixed-point tensor…

Strongly Correlated Electrons · Physics 2025-08-01 Bader Aldossari , Sergey Blinov , Zhu-Xi Luo

Any matrix product state $|\Psi\rangle$ has a set of associated kept and discarded spaces, needed for the description of $|\Psi\rangle$, and changes thereof, respectively. These induce a partition of the full Hilbert space of the system…

Quantum Physics · Physics 2023-06-01 Andreas Gleis , Jheng-Wei Li , Jan von Delft

The tensor product representation of quantum states leads to a promising variational approach to study quantum phase and quantum phase transitions, especially topological ordered phases which are impossible to handle with conventional…

Strongly Correlated Electrons · Physics 2013-05-29 Xie Chen , Bei Zeng , Zheng-Cheng Gu , Isaac L. Chuang , Xiao-Gang Wen

We examine noninvertible symmetry (NIS) in one-dimensional (1D) symmetry-protected topological (SPT) phases protected by dipolar and exponential-charge symmetries, which are two key examples of modulated SPT (MSPT). To set the stage, we…

Strongly Correlated Electrons · Physics 2025-10-29 Jintae Kim , Yizhi You , Jung Hoon Han

The theory of entanglement provides a fundamentally new language for describing interactions and correlations in many body systems. Its vocabulary consists of qubits and entangled pairs, and the syntax is provided by tensor networks. We…

Quantum Physics · Physics 2021-12-20 Ignacio Cirac , David Perez-Garcia , Norbert Schuch , Frank Verstraete

Neural-network quantum states (NQS) have become a powerful tool in many-body physics. Of the numerous possible architectures in which neural-networks can encode amplitudes of quantum states the simplicity of the Restricted Boltzmann Machine…

Quantum Physics · Physics 2021-09-15 Michael Y. Pei , Stephen R. Clark
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