Quantum Machine Learning Algorithms

Quantum Machine Learning Algorithms is a class of quantum algorithms that integrates principles from quantum computing and machine learning to solve learning tasks by exploiting quantum mechanical phenomena such as superposition, entanglement, and interference, thereby distinguishing itself from classical machine learning algorithms through its reliance on quantum computational models.

1.2K

Publications

79.6K

Citations

3.2K

Authors

1.2K

Institutions

Publications per year

2017–2026

1K

Authors

3.2K

  1. IK
    Iordanis Kerenidis

    Université Paris Cité, France

    15 publications · 944 citations

    14

    h-index

  2. MC
    M. Cerezo

    Los Alamos National Laboratory, United States

    12 publications · 745 citations

    12

    h-index

  3. PJ
    Patrick J. Coles

    Los Alamos National Laboratory, United States

    12 publications · 852 citations

    12

    h-index

  4. MS
    Maria Schuld

    University of KwaZulu-Natal, South Africa

    11 publications · 5K citations

    11

    h-index

  5. DT
    Dacheng Tao

    The University of Sydney, Australia

    11 publications · 389 citations

    11

    h-index

1–5 of 3.2K

Venues

  1. Physical review. A/Physical review, A

    PRA · 76 publications · 7K citations

    36

    h-index

  2. arXiv (Cornell University)

    Repository · 115 publications · 4.3K citations

    30

    h-index

  3. Quantum Machine Intelligence

    33 publications · 1.7K citations

    24

    h-index

  4. Physical Review Letters

    PRL · 27 publications · 6K citations

    24

    h-index

  5. Scientific Reports

    30 publications · 1.8K citations

    23

    h-index

1–5