TL;DR
This paper introduces a universal graph-based tensor network algorithm capable of approximating ground states on any infinite lattice or graph, enabling flexible and accurate simulations across diverse quantum many-body systems.
Contribution
The authors develop a general gPEPS algorithm with a structural-matrix framework, allowing simulation of any lattice or graph and handling large bond dimensions efficiently.
Findings
Accurately benchmarks against known models like Heisenberg and Bose-Hubbard.
Successfully studies quantum phase transitions and phase diagrams in complex lattices.
Demonstrates high accuracy and versatility across various lattice geometries.
Abstract
We present a general graph-based Projected Entangled-Pair State (gPEPS) algorithm to approximate ground states of nearest-neighbor local Hamiltonians on any lattice or graph of infinite size. By introducing the structural-matrix which codifies the details of tensor networks on any graphs in any dimension , we are able to produce a code that can be essentially launched to simulate any lattice. We further introduce an optimized algorithm to compute simple tensor updates as well as expectation values and correlators with a mean-field-like effective environments. Though not being variational, this strategy allows to cope with PEPS of very large bond dimension (e.g., ), and produces remarkably accurate results in the thermodynamic limit in many situations, and specially when the correlation length is small and the connectivity of the lattice is large. We prove the validity of our…
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