Quantum Autoencoders

Quantum Autoencoders is a quantum machine learning framework that, as a quantum analog of classical autoencoders, employs parameterized quantum circuits to compress quantum data into a lower-dimensional latent subspace by variationally minimizing reconstruction error of the original quantum state.

56

Publications

3.6K

Citations

201

Authors

127

Institutions

Publications per year

2017–2026

56

Authors

201

  1. LL
    Lucas Lamata

    University of the Basque Country, Spain

    3 publications · 134 citations

    3

    h-index

  2. AL
    Ang Li

    Pacific Northwest National Laboratory, United States

    2 publications · 89 citations

    2

    h-index

  3. IT
    Ivano Tavernelli

    École Polytechnique Fédérale de Lausanne, Switzerland

    2 publications · 50 citations

    2

    h-index

  4. SX
    Shuai Xu

    Southeast University, China

    2 publications · 89 citations

    2

    h-index

  5. QG
    Qiang Guan

    Kent State University, United States

    2 publications · 89 citations

    2

    h-index

1–5 of 201

Venues

  1. arXiv (Cornell University)

    Repository · 6 publications · 136 citations

    6

    h-index

  2. Quantum Machine Intelligence

    5 publications · 201 citations

    5

    h-index

  3. Machine Learning Science and Technology

    4 publications · 167 citations

    4

    h-index

  4. Quantum Science and Technology

    QST · 4 publications · 723 citations

    4

    h-index

  5. Advanced Quantum Technologies

    2 publications · 46 citations

    2

    h-index

1–5