Exploring corner transfer matrices and corner tensors for the classical simulation of quantum lattice systems

In this paper we explore the practical use of the corner transfer matrix and its higher-dimensional generalization, the corner tensor, to develop tensor network algorithms for the classical simulation of quantum lattice systems of infinite size. This exploration is done mainly in one and two spatial...

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Veröffentlicht in:Physical review. B, Condensed matter and materials physics Condensed matter and materials physics, 2012-05, Vol.85 (20), Article 205117
1. Verfasser: Orus, Roman
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Sprache:eng
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Zusammenfassung:In this paper we explore the practical use of the corner transfer matrix and its higher-dimensional generalization, the corner tensor, to develop tensor network algorithms for the classical simulation of quantum lattice systems of infinite size. This exploration is done mainly in one and two spatial dimensions (1D and 2D). We describe a number of numerical algorithms based on corner matrices and tensors to approximate different ground-state properties of these systems. The proposed methods also make use of matrix product operators and projected entangled pair operators and naturally preserve spatial symmetries of the system such as translation invariance. In order to assess the validity of our algorithms, we provide preliminary benchmarking calculations for the spin-1/2 quantum Ising model in a transverse field in both 1D and 2D. Our methods are a plausible alternative to other well-established tensor network approaches such as iDMRG and iTEBD in 1D, and ?PEPS and TERG in 2D. The computational complexity of the proposed algorithms is also considered and, in 2D, important differences are found depending on the chosen simulation scheme. We also discuss further possibilities, such as 3D quantum lattice systems, periodic boundary conditions, and real-time evolution. This discussion leads us to reinterpret the standard iTEBD and iPEPS algorithms in terms of corner transfer matrices and corner tensors. Our paper also offers a perspective on many properties of the corner transfer matrix and its higher-dimensional generalizations in the light of novel tensor network methods.
ISSN:1098-0121
1550-235X
DOI:10.1103/PhysRevB.85.205117