Quantum Federated Learning

Quantum Federated Learning is a distributed quantum machine learning framework that enables multiple participants to collaboratively train a shared quantum model without exchanging local quantum data, preserving data privacy through quantum information processing.

20

Publications

1.1K

Citations

80

Authors

41

Institutions

Publications per year

2017–2026

20

Authors

80

  1. SY
    Samuel Yen-Chi Chen

    Brookhaven National Laboratory, United States

    4 publications · 391 citations

    4

    h-index

  2. CL
    Chin‐Hui Lee

    Georgia Institute of Technology, United States

    2 publications · 174 citations

    2

    h-index

  3. PC
    Pin‐Yu Chen

    IBM (United States), United States

    2 publications · 174 citations

    2

    h-index

  4. CH
    Chao-Han Huck Yang

    Georgia Institute of Technology, United States

    2 publications · 174 citations

    2

    h-index

  5. SM
    Sabato Marco Siniscalchi

    Georgia Institute of Technology, United States

    2 publications · 174 citations

    2

    h-index

1–5 of 80

Venues

  1. arXiv (Cornell University)

    Repository · 3 publications · 185 citations

    3

    h-index

  2. Science China Physics Mechanics and Astronomy

    2 publications · 202 citations

    2

    h-index

  3. 2021 IEEE Global Communications Conference (GLOBECOM)

    Conference · 1 publications · 42 citations

    1

    h-index

  4. 1

    h-index

  5. 1

    h-index

1–5