Quantum Generative Models

Quantum Generative Models is a generative modeling framework in quantum machine learning that leverages quantum mechanical principles to learn and sample from the probability distribution of classical or quantum data through parameterized quantum circuits or quantum state preparation techniques.

124

Publications

11.8K

Citations

442

Authors

230

Institutions

Publications per year

2017–2026

118

Authors

442

  1. AP
    Alejandro Perdomo‐Ortiz

    Ames Research Center, United States

    11 publications · 1.2K citations

    10

    h-index

  2. MB
    Marcello Benedetti

    Cambridge Quantum Computing (United Kingdom), United Kingdom

    5 publications · 946 citations

    5

    h-index

  3. RG
    Roger G. Melko

    University of Waterloo, Canada

    4 publications · 511 citations

    4

    h-index

  4. JC
    Juan Carrasquilla

    University of Waterloo, Canada

    4 publications · 460 citations

    4

    h-index

  5. SV
    S. Vallecorsa

    Lawrence Berkeley National Laboratory, United States

    4 publications · 90 citations

    4

    h-index

1–5 of 442

Venues

  1. arXiv (Cornell University)

    Repository · 17 publications · 786 citations

    12

    h-index

  2. Physical review. A/Physical review, A

    PRA · 11 publications · 1.2K citations

    11

    h-index

  3. Quantum Science and Technology

    QST · 8 publications · 2K citations

    8

    h-index

  4. npj Quantum Information

    7 publications · 1.2K citations

    7

    h-index

  5. Physical Review Letters

    PRL · 4 publications · 751 citations

    4

    h-index

1–5