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Trainability barriers and opportunities in quantum generative modeling
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. In this work, we investigate the barriers to the trainability of quantum genera...
| Autores principales: | , , , , , , |
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| Lenguaje: | eng |
| Publicado: |
2023
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| Materias: | |
| Acceso en línea: | http://cds.cern.ch/record/2866746 |