Quantum Generative Models
Quantum Generative Models is a generative modeling framework in quantum machine learning that leverages quantum mechanical principles to learn and sample from the probability distribution of classical or quantum data through parameterized quantum circuits or quantum state preparation techniques.
124
Publications
11.8K
Citations
442
Authors
230
Institutions
Publications per year
2017–2026
118
Parents
124
Parameterized quantum circuits as machine learning models
Quantum Science and Technology · 2019 · 1K citations · Full text
Computing, Computer Science, Physics +15
Quantum Generative Adversarial Learning
Seth Lloyd, Christian Weedbrook · Physical Review Letters · 2018 · 573 citations · Full text
Computing, Computer Science, Physics +14
1–5 of 124
442
10
h-index
Cambridge Quantum Computing (United Kingdom), United Kingdom
5 publications · 946 citations
5
h-index
4
h-index
4
h-index
4
h-index
1–5 of 442
230
University College London
London, United Kingdom
11 h-index
19 publications
3.8K citations

University of Waterloo
Waterloo, Canada
9 h-index
24 publications
3.6K citations
Zapata (United States)
Boston, United States
8 h-index
15 publications
1.2K citations
University of Chinese Academy of Sciences
Beijing, China
8 h-index
15 publications
1.5K citations

Perimeter Institute
Waterloo, Canada
8 h-index
14 publications
1.4K citations
Flatiron Health (United States)
New York, United States
7 h-index
7 publications
1.2K citations
Cambridge Quantum Computing (United Kingdom)
Cambridge, United Kingdom
7 h-index
14 publications
5.6K citations
Vector Institute
Toronto, Canada
7 h-index
8 publications
617 citations
1–8 of 230
Repository · 17 publications · 786 citations
12
h-index
PRA · 11 publications · 1.2K citations
11
h-index
QST · 8 publications · 2K citations
8
h-index
7 publications · 1.2K citations
7
h-index
PRL · 4 publications · 751 citations
4
h-index
1–5