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CERN Document Server 找到 2 笔记录  检索需时 0.59 秒. 
1.
Latent Style-based Quantum GAN for high-quality Image Generation / Chang, Su Yeon (CERN ; Ecole Polytechnique, Lausanne) ; Thanasilp, Supanut (Ecole Polytechnique, Lausanne ; Chulalongkorn U. ; European Space Agency) ; Le Saux, Bertrand (European Space Agency) ; Vallecorsa, Sofia (CERN) ; Grossi, Michele (CERN)
Quantum generative modeling is among the promising candidates for achieving a practical advantage in data analysis. [...]
arXiv:2406.02668. - 28 p.
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2.
Trainability barriers and opportunities in quantum generative modeling / Rudolph, Manuel S. (Ecole Polytechnique, Lausanne) ; Lerch, Sacha (Ecole Polytechnique, Lausanne) ; Thanasilp, Supanut (Ecole Polytechnique, Lausanne ; Chulalongkorn U.) ; Kiss, Oriel (CERN ; Geneva U.) ; Shaya, Oxana (LPHE, Lausanne) ; Vallecorsa, Sofia (CERN) ; Grossi, Michele (CERN) ; Holmes, Zoë (Ecole Polytechnique, Lausanne)
Quantum generative models, in providing inherently efficient sampling strategies, show promise for achieving a near-term advantage on quantum hardware. Nonetheless, important questions remain regarding their scalability. [...]
arXiv:2305.02881.- 2024-11-13 - 52 p. - Published in : npj Quantum Inf. 10 (2024) 116 Fulltext: 2305.02881 - PDF; document - PDF;
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