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qrennd

The Quantum REcurrent Neural Network Decoder (QRENND).

https://github.com/borisvarbanov/qrennd

Science Score: 44.0%

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    CITATION.cff file
    Found CITATION.cff file
  • ✓
    codemeta.json file
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    .zenodo.json file
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    Scientific vocabulary similarity
    Low similarity (0.9%) to scientific vocabulary
Last synced: 6 months ago · JSON representation ·

Repository

The Quantum REcurrent Neural Network Decoder (QRENND).

Basic Info
  • Host: GitHub
  • Owner: BorisVarbanov
  • License: apache-2.0
  • Language: Python
  • Default Branch: main
  • Size: 891 KB
Statistics
  • Stars: 8
  • Watchers: 1
  • Forks: 0
  • Open Issues: 0
  • Releases: 1
Created over 3 years ago · Last pushed 6 months ago
Metadata Files
Readme License Citation

README.md

QReNND

The Quantum Recurrent Neural Network Decoder (QReNND) is a python package implementing a long-term short-term (LSTM) memory Recurrent Neural Network for decoding quantum error correcting codes.

Owner

  • Name: b.m.varbanov
  • Login: BorisVarbanov
  • Kind: user
  • Location: Netherlands

Applied Physics student at TU Delft

Citation (CITATION.cff) 

# This CITATION.cff file was generated with cffinit.
# Visit https://bit.ly/cffinit to generate yours today!

cff-version: 1.2.0
title: qrennd
message: >-
  A Python package implementing a recurrent neural network
  decoder for surface-code memory experiments
type: software
authors:
  - given-names: Boris Mihailov
    family-names: Varbanov
    email: b.m.varbanov@tudelft.nl
    affiliation: 'QuTech, Delft University of Technology'
    orcid: 'https://orcid.org/0000-0001-7124-8933'
  - given-names: Marc
    family-names: Serra-Peralta
    email: M.SerraPeralta@student.tudelft.nl
    affiliation: 'QuTech, Delft University of Technology'
    orcid: 'https://orcid.org/0000-0002-8000-8701'
repository-code: 'https://github.com/BorisVarbanov/qrennd'
abstract: >-
  Neural-network decoders can achieve a lower logical error
  rate compared to conventional decoders, like
  minimum-weight perfect matching, when decoding the surface
  code. Furthermore, these decoders require no prior
  information about the physical error rates, making them
  highly adaptable.


  In this package, we implement a simple recurrent neural
  network using the Tensorflow library.
keywords:
  - quantum
  - quantum-computing
  - quantum-error-correction
  - surface-code
  - neural-network-decoder
license: Apache-2.0
commit: 4c86873
version: '1'
date-released: '2023-07-07'

GitHub Events

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  • Push event: 2
Last Year
  • Watch event: 3
  • Push event: 2