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of 5
pro vyhledávání: '"Monbroussou, Leo"'
We study the convergence properties of Variational Quantum Circuits (VQCs) to investigate how they can differ from their classical counterparts. It is known that a VQC is a linear model in a feature map determined by its architecture. Learning a clas
Externí odkaz:
http://arxiv.org/abs/2411.04940
Autor:
Monbroussou, Léo, Mamon, Eliott Z., Thomas, Hugo, Yacoub, Verena, Chabaud, Ulysse, Kashefi, Elham
We propose a new scheme for near-term photonic quantum device that allows to increase the expressive power of the quantum models beyond what linear optics can do. This scheme relies upon state injection, a measurement-based technique that can produce
Externí odkaz:
http://arxiv.org/abs/2410.01572
Subspace preserving quantum circuits are a class of quantum algorithms that, relying on some symmetries in the computation, can offer theoretical guarantees for their training. Those algorithms have gained extensive interest as they can offer polynom
Externí odkaz:
http://arxiv.org/abs/2409.18918
Autor:
Mhiri, Hela, Monbroussou, Leo, Herrero-Gonzalez, Mario, Thabet, Slimane, Kashefi, Elham, Landman, Jonas
In this work, we highlight an unforeseen behavior of the expressivity of Parameterized Quantum Circuits (PQC) for machine learning. A large class of these models, seen as Fourier Series which frequencies are derived from the encoding gates, were thou
Externí odkaz:
http://arxiv.org/abs/2403.09417
Autor:
Monbroussou, Léo, Mamon, Eliott Z., Landman, Jonas, Grilo, Alex B., Kukla, Romain, Kashefi, Elham
Quantum machine learning (QML) has become a promising area for real world applications of quantum computers, but near-term methods and their scalability are still important research topics. In this context, we analyze the trainability and controllabi
Externí odkaz:
http://arxiv.org/abs/2309.15547