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qkt_benchmarking

Source code for reproducibility of experiments at https://doi.org/10.48550/arXiv.2408.10274

https://github.com/diegoalvareze/qkt_benchmarking

Science Score: 54.0%

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    Found CITATION.cff file
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    .zenodo.json file
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    DOI references
    Found 4 DOI reference(s) in README
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    Academic publication links
    Links to: arxiv.org
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    Low similarity (2.1%) to scientific vocabulary
Last synced: 6 months ago · JSON representation ·

Repository

Source code for reproducibility of experiments at https://doi.org/10.48550/arXiv.2408.10274

Basic Info
  • Host: GitHub
  • Owner: diegoalvareze
  • License: apache-2.0
  • Language: Jupyter Notebook
  • Default Branch: main
  • Size: 3.24 MB
Statistics
  • Stars: 0
  • Watchers: 1
  • Forks: 0
  • Open Issues: 0
  • Releases: 0
Created about 1 year ago · Last pushed about 1 year ago
Metadata Files
Readme License Citation

README.md

qkt_benchmarking

Source code for reproducibility of experiments described in the following paper:

D. Alvarez-Estevez, "Benchmarking Quantum Machine Learning Kernel Training for Classification Tasks," in IEEE Transactions on Quantum Engineering, vol. 6, pp. 1-15, 2025, Art no. 2500215, https://doi.org/10.1109/TQE.2025.3541882.

Related ArXiv.org pre-print: https://doi.org/10.48550/arXiv.2408.10274

Owner

  • Name: Diego Alvarez-Estevez
  • Login: diegoalvareze
  • Kind: user

Citation (CITATION.bib) 

@ARTICLE{10884820,
  author={Alvarez-Estevez, Diego},
  journal={IEEE Transactions on Quantum Engineering}, 
  title={Benchmarking Quantum Machine Learning Kernel Training for Classification Tasks}, 
  year={2025},
  volume={6},
  number={},
  pages={1-15},
  keywords={Kernel;Benchmark testing;Machine learning;Vectors;Training;Quantum computing;Estimation;Quantum mechanics;Support vector machines;Quantum state;Benchmarking;quantum kernel estimation (QKE);quantum kernel training (QKT), quantum machine learning (QML)},
  doi={10.1109/TQE.2025.3541882}}

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Dependencies

requirements.txt pypi
  • matplotlib ==3.8.4
  • numpy ==2.2.1
  • pandas ==2.2.3
  • qiskit ==1.1.0
  • qiskit_algorithms ==0.3.0
  • qiskit_machine_learning ==0.7.2
  • scikit_learn ==1.4.2
  • scipy ==1.14.1