Hybrid Quantum-classical Learning

Hybrid Quantum-Classical Learning is a computational learning paradigm that integrates quantum and classical processing components in a unified architecture, distinguishing itself from purely classical or purely quantum approaches by leveraging quantum resources for specific subroutines while relying on classical systems for control, optimization, or data preprocessing.

191

Publications

16.7K

Citations

680

Authors

314

Institutions

Publications per year

2017–2026

188

Authors

680

  1. SY
    Samuel Yen-Chi Chen

    Brookhaven National Laboratory, United States

    6 publications · 518 citations

    6

    h-index

  2. XW
    Xin Wang

    Baidu (China), China

    4 publications · 143 citations

    4

    h-index

  3. AP
    Alejandro Perdomo‐Ortiz

    Ames Research Center, United States

    4 publications · 935 citations

    4

    h-index

  4. TL
    Tongliang Liu

    The University of Sydney, Australia

    4 publications · 162 citations

    4

    h-index

  5. SY
    Shinjae Yoo

    Brookhaven National Laboratory, United States

    4 publications · 329 citations

    4

    h-index

1–5 of 680

Venues

  1. arXiv (Cornell University)

    Repository · 31 publications · 1.4K citations

    15

    h-index

  2. Physical review. A/Physical review, A

    PRA · 13 publications · 1.6K citations

    12

    h-index

  3. Quantum Science and Technology

    QST · 8 publications · 1.8K citations

    8

    h-index

  4. Scientific Reports

    8 publications · 264 citations

    8

    h-index

  5. Electronics

    5 publications · 225 citations

    5

    h-index

1–5