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re-qgan

https://github.com/sandra-nguemto/re-qgan

Science Score: 54.0%

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    CITATION.cff file
    Found CITATION.cff file
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    codemeta.json file
    Found codemeta.json file
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    .zenodo.json file
    Found .zenodo.json file
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    DOI references
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    Academic publication links
    Links to: arxiv.org
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    Academic email domains
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    JOSS paper metadata
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    Scientific vocabulary similarity
    Low similarity (10.3%) to scientific vocabulary

Keywords

gan machine-learning qiskit quantum-machine-learning
Last synced: 6 months ago · JSON representation ·

Repository

Basic Info
  • Host: GitHub
  • Owner: sandra-nguemto
  • License: mit
  • Language: Jupyter Notebook
  • Default Branch: main
  • Homepage:
  • Size: 13.3 MB
Statistics
  • Stars: 1
  • Watchers: 1
  • Forks: 0
  • Open Issues: 0
  • Releases: 0
Topics
gan machine-learning qiskit quantum-machine-learning
Created over 2 years ago · Last pushed about 1 year ago
Metadata Files
Readme License Citation

README.md

Re-QGAN: an optimized adversarial quantum circuit learning framework

Adversarial learning represents a powerful technique for generating data statistics. Its successful implementation in quantum computational platforms is not straightforward due to limitations in connectivity, quantum operation fidelity, and limited access to the quantum processor for statistically relevant results. Constraining the number of quantum operations and providing a design with a low compilation cost, this code creates a quantum generative adversarial network design that uses real Hilbert spaces as the framework for the generative model and a novel strategy to encode classical information into the quantum framework. For more information check "Re-QGAN: an optimized adversarial quantum circuit learning framework" (https://arxiv.org/abs/2208.02165).

Usage

Dependencies

Developer - Oak Ridge National Laboratory

  • [ ] Sandra Nguemto anguemt1@jh.edu
  • [ ] Vicente Leyton-Ortega vlk@ornl.gov

Funding

The Re-QGAN project is supported by the DOE Advanced Scientific Computing Research (ASCR) Accelerated Research in Quantum Computing (ARQC) Program at Oak Ridge National Laboratory under field work proposal ERKJ354.

License

Citation

bibtex @software{Nguemto_Re-QGAN_2023, author = {Nguemto, Sandra and Leyton-Ortega, Vicente}, month = aug, title = {{Re-QGAN}}, url = {https://github.com/sandra-nguemto/Re-QGAN}, year = {2023} }

Owner

  • Name: Sandra Nguemto
  • Login: sandra-nguemto
  • Kind: user
  • Company: University of Tennessee

Citation (citation.cff) 

cff-version: 1.2.0
message: "If you use this software, please cite it as below."
authors:
- family-names: "Nguemto"
  given-names: "Sandra"
  orcid: ""
- family-names: "Leyton-Ortega"
  given-names: "Vicente"
  orcid: ""
title: "Re-QGAN"
version: 
doi: 
date-released: 2023-08-14
url: "https://github.com/sandra-nguemto/Re-QGAN"

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