Build your own tensor network library: DMRjulia I. Basic library for the density matrix renormalization group

TL;DR

This paper introduces a foundational Julia library for tensor network computations focused on the density matrix renormalization group, providing educational code examples, implementation details, and a basis for research and algorithm development.

Contribution

It presents a basic, efficient Julia library for tensor network operations tailored to DMRG, serving as an educational and research tool for the community.

Findings

Library enables understanding of tensor network computations.

Code is optimized for research and algorithm development.

Provides comprehensive educational material.

Abstract

An introduction to the density matrix renormalization group is contained here, including coding examples. The focus of this code is on basic operations involved in tensor network computations, and this forms the foundation of the DMRjulia library. Algorithmic complexity, measurements from the matrix product state, convergence to the ground state, and other relevant features are also discussed. The present document covers the implementation of operations for dense tensors into the Julia language. The code can be used as an educational tool to understand how tensor network computations are done in the context of entanglement renormalization or as a template for other codes in low level languages. A comprehensive Supplemental Material is meant to be a "Numerical Recipes" style introduction to the core functions and a simple implementation of them. The code is fast enough to be used in research and can be used to make new algorithms.