Quantum Circuit Training

Quantum Circuit Training is a computational method in quantum machine learning that involves optimizing the parameters of a quantum circuit through iterative evaluation and adjustment to minimize a cost function, thereby enabling the circuit to perform a desired quantum computation or learning task.

175

Publications

16K

Citations

444

Authors

263

Institutions

Publications per year

2017–2026

163

Authors

444

  1. PJ
    Patrick J. Coles

    Los Alamos National Laboratory, United States

    7 publications · 1.2K citations

    7

    h-index

  2. ŁC
    Łukasz Cincio

    Los Alamos National Laboratory, United States

    6 publications · 647 citations

    6

    h-index

  3. DZ
    David Z. Pan

    The University of Texas at Austin, United States

    5 publications · 300 citations

    6

    h-index

  4. MC
    M. Cerezo

    Los Alamos National Laboratory, United States

    6 publications · 1.2K citations

    6

    h-index

  5. SH
    Song Han

    Massachusetts Institute of Technology, United States

    5 publications · 300 citations

    6

    h-index

1–5 of 444

Venues

  1. arXiv (Cornell University)

    Repository · 22 publications · 818 citations

    17

    h-index

  2. Physical review. A/Physical review, A

    PRA · 15 publications · 3.9K citations

    14

    h-index

  3. PRX Quantum

    7 publications · 973 citations

    7

    h-index

  4. Quantum Science and Technology

    QST · 6 publications · 349 citations

    7

    h-index

  5. Quantum

    6 publications · 850 citations

    7

    h-index

1–5