Quantum Learning Theory

Quantum Learning Theory is a theoretical framework within quantum machine learning that formalizes the principles and limits of learning from quantum data, distinguishing itself by integrating quantum computational models with statistical learning theory to analyze the learnability of quantum processes and states.

318

Publications

20.4K

Citations

881

Authors

419

Institutions

Publications per year

2017–2026

223

Authors

881

  1. RG
    Roberto Giuntini

    University of Cagliari, Italy

    6 publications · 196 citations

    6

    h-index

  2. VD
    Vedran Dunjko

    Leiden University, Netherlands

    6 publications · 669 citations

    6

    h-index

  3. GS
    Giuseppe Sergioli

    University of Cagliari, Italy

    6 publications · 196 citations

    6

    h-index

  4. HJ
    Hans J. Briegel

    Universität Innsbruck, Austria

    6 publications · 669 citations

    6

    h-index

  5. FP
    Francesco Petruccione

    University of KwaZulu-Natal, South Africa

    5 publications · 1.2K citations

    5

    h-index

1–5 of 881

Venues

  1. arXiv (Cornell University)

    Repository · 37 publications · 1.2K citations

    19

    h-index

  2. Quantum Information Processing

    11 publications · 995 citations

    11

    h-index

  3. Physical review. A/Physical review, A

    PRA · 12 publications · 676 citations

    11

    h-index

  4. PRX Quantum

    8 publications · 896 citations

    8

    h-index

  5. Quantum Machine Intelligence

    8 publications · 340 citations

    8

    h-index

1–5