DMRjulia: Tensor recipes for entanglement renormalization computations

TL;DR

This paper introduces the DMRjulia library, a Julia-based toolkit for tensor network computations focused on entanglement renormalization, providing detailed documentation and best practices for implementing the density matrix renormalization group algorithm.

Contribution

It offers a comprehensive, high-level Julia library for tensor network computations, with detailed implementation notes and guidance for the density matrix renormalization group method.

Findings

Provides a Julia library for tensor network algorithms

Includes detailed implementation notes and best practices

Focuses on the density matrix renormalization group

Abstract

Detailed notes on the functions included in the DMRjulia library are included here. This discussion of how to program functions for a tensor network library are intended to be a supplement to the other documentation dedicated to explaining the high level concepts. The chosen language used here is the high-level julia language that is intended to provide an introduction to provide a concise introduction and show transparently some best practices for the functions. This document is best used as a supplement to both the internal code notes and introductions to the subject to both inform the user about other functions available and also to clarify some design choices and future directions. This document presently covers the implementation of the functions in the tensor network library for dense tensors. The algorithms implemented here is the density matrix renormalization group. The document will be updated periodically with new features to include the latest developments.