Zobrazeno 1 - 10
of 24
pro vyhledávání: '"Elies G"'
Publikováno v:
Knowledge and Management of Aquatic Ecosystems, Vol 0, Iss 350-351, Pp 623-634 (1998)
Les IGF, leurs récepteurs et leurs protéines de liaison constituent une famille moléculaire qui joue un rôle essentiel dans la régulation de la croissance et du développement. Nous nous sommes intéressés à la caractérisation moléculaire de
Externí odkaz:
https://doaj.org/article/937f7abb3cac4d3d8d6304708842ea67
Publikováno v:
Nature Communications, Vol 15, Iss 1, Pp 1-12 (2024)
Abstract Quantum machine learning models have shown successful generalization performance even when trained with few data. In this work, through systematic randomization experiments, we show that traditional approaches to understanding generalization
Externí odkaz:
https://doaj.org/article/ad24075e10ed421eb0c7b86d28fd4319
Publikováno v:
In General and Comparative Endocrinology 2002 126(3):269-278
Publikováno v:
In Molecular and Cellular Endocrinology 1999 158(1):173-185
Publikováno v:
Machine Learning: Science and Technology, Vol 5, Iss 2, p 025003 (2024)
One of the most natural connections between quantum and classical machine learning has been established in the context of kernel methods. Kernel methods rely on kernels, which are inner products of feature vectors living in large feature spaces. Quan
Externí odkaz:
https://doaj.org/article/751cb4f69dd4435e8e04dd9e349286dc
Autor:
Johannes Jakob Meyer, Marian Mularski, Elies Gil-Fuster, Antonio Anna Mele, Francesco Arzani, Alissa Wilms, Jens Eisert
Publikováno v:
PRX Quantum, Vol 4, Iss 1, p 010328 (2023)
Variational quantum machine learning is an extensively studied application of near-term quantum computers. The success of variational quantum learning models crucially depends on finding a suitable parametrization of the model that encodes an inducti
Externí odkaz:
https://doaj.org/article/30fd87833687421e9c5d6a5358a5b3de
Publikováno v:
Quantum, Vol 5, p 582 (2021)
A large body of recent work has begun to explore the potential of parametrized quantum circuits (PQCs) as machine learning models, within the framework of hybrid quantum-classical optimization. In particular, theoretical guarantees on the out-of-samp
Externí odkaz:
https://doaj.org/article/fb1662ccb5434e1ba0fb7ee8d65d5868
Autor:
ELIES, G., DUVAL, H., GROIGNO, L., WOLFF, J., BOEUF, G., BOUJARD, D., ELIES, G., DUVAL, H., GROIGNO, L., WOLFF, J., BOEUF, G., BOUJARD, D.
Publikováno v:
Bulletin Français de la Pêche et de la Pisciculture; January 1998, Vol. 1998 Issue: 350 p623-634, 12p
Publikováno v:
Quantum, Vol 4, p 226 (2020)
A single qubit provides sufficient computational capabilities to construct a universal quantum classifier when assisted with a classical subroutine. This fact may be surprising since a single qubit only offers a simple superposition of two states and
Externí odkaz:
https://doaj.org/article/31766b2340c8496e80e70fe243bc993f
Publikováno v:
Molecular and Cellular Endocrinology; 1996, Vol. 124 Issue: 1 p131-140, 10p