TensorNetworkCodes.jl

KO | EN

gitlite — search

#javascript #python #hacktoberfest #react #ai #typescript #llm #go #golang #android #machine-learning #rust #deep-learning #linux

TensorNetworkCodes.jl

★ 10 Open GitHub ↗

TensorNetworkCodes is a Julia library developed to support the following research: https://arxiv.org/abs/2109.11996

// repository documentation

Was this content helpful?

★ 0 (0 ratings)

# TensorNetworkCodes

[![Stable](https://img.shields.io/badge/docs-stable-blue.svg)](https://qecsim.github.io/TensorNetworkCodes.jl/stable)
[![Dev](https://img.shields.io/badge/docs-dev-blue.svg)](https://qecsim.github.io/TensorNetworkCodes.jl/dev)
[![Build Status](https://github.com/qecsim/TensorNetworkCodes.jl/workflows/CI/badge.svg)](https://github.com/qecsim/TensorNetworkCodes.jl/actions)
[![Coverage](https://codecov.io/gh/qecsim/TensorNetworkCodes.jl/branch/main/graph/badge.svg?token=M4ATXN9DIK)](https://codecov.io/gh/qecsim/TensorNetworkCodes.jl)

## Introduction

[TensorNetworkCodes.jl](https://github.com/qecsim/TensorNetworkCodes.jl) is a Julia library
developed to support the following research:

* T. Farrelly, D. K. Tuckett, T. M. Stace, _Local tensor-network codes_,
  [arXiv:2109.11996](https://arxiv.org/abs/2109.11996), (2021).

## Installation

_TensorNetworkCodes.jl_ is installed, like any other registered Julia package,
using the Julia package manager [Pkg](https://pkgdocs.julialang.org/):

```julia
pkg> add TensorNetworkCodes  # Press ']' to enter the Pkg REPL mode.
```
or
```julia
julia> using Pkg; Pkg.add("TensorNetworkCodes")
```

## Demos

The following demos correspond to results included in
[arXiv:2109.11996](https://arxiv.org/abs/2109.11996):

* [Small example tensor-network codes and code distances](https://nbviewer.org/github/qecsim/TensorNetworkCodes.jl/blob/main/nbs/Small_examples_with_distance.ipynb)

* [The 19 qubit colour code as a tensor-network code](https://nbviewer.org/github/qecsim/TensorNetworkCodes.jl/blob/main/nbs/Colour_code.ipynb)

* [The modified surface code](https://nbviewer.org/github/qecsim/TensorNetworkCodes.jl/blob/main/nbs/Modified_surface_code_example.ipynb)

## Citing

Please cite _TensorNetworkCodes.jl_ if you use it in your research.

A suitable BibTeX entry is:

@article{Farrelly_LocalTNCodes_2021,
        title = {Local tensor-network codes},
        author = {Farrelly, Terry and Tuckett, David K. and Stace, Thomas M.},
        year = {2021},
        archiveprefix = {arXiv},
        eprint = {2109.11996},
        url = {https://arxiv.org/abs/2109.11996},
    }

Similarly, please cite [Qecsim.jl](https://github.com/qecsim/Qecsim.jl) if you use its
features in your research, see
[Qecsim.jl Documentation](https://qecsim.github.io/Qecsim.jl/) for details.

## License

_TensorNetworkCodes.jl_ is released under the BSD 3-Clause license, see
[LICENSE](https://github.com/qecsim/TensorNetworkCodes.jl/blob/main/LICENSE).

## Links

* Source code: <https://github.com/qecsim/TensorNetworkCodes.jl>
* Documentation: <https://qecsim.github.io/TensorNetworkCodes.jl>
* Contact: Terry Farrelly [farreltc@tcd.ie](mailto:farreltc@tcd.ie)

TensorNetworkCodes.jl

Explore Similar Repositories

Was this content helpful?

Recent Feedback

Download README