Abstract

We use the formalism of tensor network states to investigate the relation between static correlation functions in the ground state of local quantum many-body Hamiltonians and the dispersion relations of the corresponding low-energy excitations. In particular, we show that the matrix product state transfer matrix (MPS-TM)—a central object in the computation of static correlation functions—provides important information about the location and magnitude of the minima of the low-energy dispersion relation(s), and we present supporting numerical data for one-dimensional lattice and continuum models as well as two-dimensional lattice models on a cylinder. We elaborate on the peculiar structure of the MPS-TM’s eigenspectrum and give several arguments for the close relation between the structure of the low-energy spectrum of the system and the form of the static correlation functions. Finally, we discuss how the MPS-TM connects to the exact quantum transfer matrix of the model at zero temperature. We present a renormalization group argument for obtaining finite bond dimension approximations of the MPS, which allows one to reinterpret variational MPS techniques (such as the density matrix renormalization group) as an application of Wilson’s numerical renormalization group along the virtual (imaginary time) dimension of the system.

Highlights

  • Determining the vacuum of an interacting field theory or the ground state of a strongly interacting quantum system described by a local translational invariant Hamiltonian is one of the most fundamental and challenging tasks in quantum many-body physics

  • In this paper we have investigated how much information about the excitation spectrum of a local translationinvariant Hamiltonian can be obtained from local information and static correlations in the ground state

  • We have approached this question using the formalism of tensor network states in particular, but have established several general results not restricted to tensor network formulations

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Summary

May 2015

V Zauner, D Draxler, L Vanderstraeten, M Degroote, J Haegeman, M M Rams, V Stojevic, N Schuch and F Verstraete.

Introduction
Tensor network transfer matrices
Numerical results
Two-dimensional lattice models
Static correlation functions and excitations
Imaginary time evolution as tensor network
Conclusions
Findings
Proof We start by defining AX as and first show that
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