showing 5 related works from this author
Benchmarking global SU(2) symmetry in two-dimensional tensor network algorithms
2020
We implement and benchmark tensor network algorithms with $SU(2)$ symmetry for systems in two spatial dimensions and in the thermodynamic limit. Specifically, we implement $SU(2)$-invariant versions of the infinite projected entangled pair states and infinite projected entangled simplex states methods. Our implementation of $SU(2)$ symmetry follows the formalism based on fusion trees from Schmoll et al. [Ann. Phys. 419, 168232 (2020)]. In order to assess the utility of implementing $SU(2)$ symmetry, the algorithms are benchmarked for three models with different local spin: the spin-1 bilinear-biquadratic model on the square lattice, and the kagome Heisenberg antiferromagnets (KHAFs) for spi…
The classical two-dimensional Heisenberg model revisited: An $SU(2)$-symmetric tensor network study
2021
The classical Heisenberg model in two spatial dimensions constitutes one of the most paradigmatic spin models, taking an important role in statistical and condensed matter physics to understand magnetism. Still, despite its paradigmatic character and the widely accepted ban of a (continuous) spontaneous symmetry breaking, controversies remain whether the model exhibits a phase transition at finite temperature. Importantly, the model can be interpreted as a lattice discretization of the $O(3)$ non-linear sigma model in $1+1$ dimensions, one of the simplest quantum field theories encompassing crucial features of celebrated higher-dimensional ones (like quantum chromodynamics in $3+1$ dimensio…
Fine Grained Tensor Network Methods.
2020
We develop a strategy for tensor network algorithms that allows to deal very efficiently with lattices of high connectivity. The basic idea is to fine-grain the physical degrees of freedom, i.e., decompose them into more fundamental units which, after a suitable coarse-graining, provide the original ones. Thanks to this procedure, the original lattice with high connectivity is transformed by an isometry into a simpler structure, which is easier to simulate via usual tensor network methods. In particular this enables the use of standard schemes to contract infinite 2d tensor networks - such as Corner Transfer Matrix Renormalization schemes - which are more involved on complex lattice structu…
Quantum criticality on a chiral ladder: An SU(2) infinite density matrix renormalization group study
2019
In this paper we study the ground-state properties of a ladder Hamiltonian with chiral $\text{SU}(2)$-invariant spin interactions, a possible first step toward the construction of truly two-dimensional nontrivial systems with chiral properties starting from quasi-one-dimensional ones. Our analysis uses a recent implementation by us of $\text{SU}(2)$ symmetry in tensor network algorithms, specifically for infinite density matrix renormalization group. After a preliminary analysis with Kadanoff coarse graining and exact diagonalization for a small-size system, we discuss its bosonization and recap the continuum limit of the model to show that it corresponds to a conformal field theory, in agr…
A programming guide for tensor networks with global SU(2) symmetry
2020
Abstract This paper is a manual with tips and tricks for programming tensor network algorithms with global S U ( 2 ) symmetry. We focus on practical details that are many times overlooked when it comes to implementing the basic building blocks of codes, such as useful data structures to store the tensors, practical ways of manipulating them, and adapting typical functions for symmetric tensors. Here we do not restrict ourselves to any specific tensor network method, but keep always in mind that the implementation should scale well for simulations of higher-dimensional systems using, e.g., Projected Entangled Pair States, where tensors with many indices may show up. To this end, the structur…