Quick Navigation

Topics

Quantum Machine Learning Quantum Simulation Entanglement Theory Quantum Correlations Quantum State Preparation Representation

DMRjulia: Tensor recipes for entanglement renormalization computations

arXiv
Authors: Thomas E. Baker, Martin P. Thompson

Year

2021

Paper ID

41189

Status

Preprint

Abstract Read

~2 min

Abstract Words

152

Citations

N/A

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.

Paper Tools

Become a member to use research tools

Sign in to open papers, visit source links, share, cite, compare, copy DOI links, request category corrections, and build your reading list.

Show Paper arXiv Publisher Share Cite This Paper Copy URL Compare Copy DOI Add to Reading List Category Correction Request

References & Citation Signals

Local Citation Graph (Related-Paper Links)

Current Paper #41189 #67310 Women for Quantum -- Manifesto ... #67301 Daemonic quantum battery charge... #67285 Assessing the Role of Communica... #67361 The Channel Capacity of a Relat...

External citation index: OpenAlex citation signal

Community Reactions

Quick sentiment from readers on this paper.

Score: 0
Likes: 0 Dislikes: 0

Sign in to react to this paper.

Discussion & Reviews (Moderated)

Average Rating: 0.0 / 5 (0 ratings)

No written reviews yet.