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pro vyhledávání: '"Manuel S. Rudolph"'
Autor:
Manuel S. Rudolph, Sacha Lerch, Supanut Thanasilp, Oriel Kiss, Oxana Shaya, Sofia Vallecorsa, Michele Grossi, Zoë Holmes
Publikováno v:
npj Quantum Information, Vol 10, Iss 1, Pp 1-18 (2024)
Abstract Quantum generative models provide inherently efficient sampling strategies and thus show promise for achieving an advantage using quantum hardware. In this work, we investigate the barriers to the trainability of quantum generative models po
Externí odkaz:
https://doaj.org/article/2dd04260c8534dd988845c48f77051cf
Autor:
Manuel S. Rudolph, Jacob Miller, Danial Motlagh, Jing Chen, Atithi Acharya, Alejandro Perdomo-Ortiz
Publikováno v:
Nature Communications, Vol 14, Iss 1, Pp 1-10 (2023)
Abstract Parametrized quantum circuits (PQCs) represent a promising framework for using present-day quantum hardware to solve diverse problems in materials science, quantum chemistry, and machine learning. We introduce a “synergistic” approach th
Externí odkaz:
https://doaj.org/article/73ea361e7bdf45a5b7cc606099283ecd
Autor:
Manuel S. Rudolph, Ntwali Bashige Toussaint, Amara Katabarwa, Sonika Johri, Borja Peropadre, Alejandro Perdomo-Ortiz
Publikováno v:
Physical Review X, Vol 12, Iss 3, p 031010 (2022)
Generating high-quality data (e.g., images or video) is one of the most exciting and challenging frontiers in unsupervised machine learning. Utilizing quantum computers in such tasks to potentially enhance conventional machine-learning algorithms has
Externí odkaz:
https://doaj.org/article/c733e24562fd4eafb429af6f32f4d120