Quantum Optimization For Machine Learning

Quantum Optimization for Machine Learning is a computational methodology that integrates quantum algorithms to enhance the efficiency and scalability of optimizing machine learning models, distinguished by its exploitation of quantum mechanical principles to solve optimization problems intractable for classical methods.

172

Publications

15.3K

Citations

561

Authors

331

Institutions

Publications per year

2017–2026

146

Authors

561

  1. SB
    Siddhartha Bhattacharyya

    University of Illinois Chicago, United States

    5 publications · 118 citations

    5

    h-index

  2. HN
    Hartmut Neven

    Google (United States), United States

    4 publications · 2.2K citations

    4

    h-index

  3. GS
    Giuseppe Sergioli

    University of Cagliari, Italy

    4 publications · 128 citations

    4

    h-index

  4. SD
    Sandip Dey

    Jadavpur University, India

    4 publications · 67 citations

    4

    h-index

  5. RG
    Roberto Giuntini

    University of Cagliari, Italy

    4 publications · 128 citations

    4

    h-index

1–5 of 561

Venues

  1. arXiv (Cornell University)

    Repository · 10 publications · 291 citations

    10

    h-index

  2. Physical review. A/Physical review, A

    PRA · 6 publications · 1.4K citations

    6

    h-index

  3. Quantum Information Processing

    5 publications · 100 citations

    5

    h-index

  4. Applied Soft Computing

    5 publications · 390 citations

    5

    h-index

  5. IEEE Access

    5 publications · 220 citations

    5

    h-index

1–5