20 citations
Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
- Centre National de la Recherche ScientifiqueFR5 papers
- LIP6FR4 papers
- Sorbonne UniversitéFR4 papers
- University of EdinburghGB4 papers
- Département d'InformatiqueFR2 papers
- École Normale Supérieure - PSLFR2 papers
- Laboratoire d’Informatique et SystèmesFR2 papers
- Aix-Marseille UniversitéFR1 paper
- CEA Paris-SaclayFR1 paper
- Centre de physique des particules de MarseilleFR1 paper
- Commissariat à l'Énergie Atomique et aux Énergies AlternativesFR1 paper
- École Nationale Supérieure de Techniques AvancéesFR1 paper
5 papers · 1 filter
When Quantum and Classical Models Disagree: Learning Beyond Minimum Norm Least Square
Slimane Thabet, Léo Monbroussou, Eliott Z. Mamon +1
Quantum Machine Learning algorithms based on Variational Quantum Circuits (VQCs) are important candidates for useful application of quantum computing. It is known that a VQC is a l…
Towards quantum advantage with photonic state injection
Léo Monbroussou, Eliott Z. Mamon, Hugo Thomas +3
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 relie…
Subspace Preserving Quantum Convolutional Neural Network Architectures
Léo Monbroussou, Jonas Landman, Letao Wang +2
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. Tho…
Constrained and Vanishing Expressivity of Quantum Fourier Models
Hela Mhiri, Leo Monbroussou, Mario Herrero-Gonzalez +3
In this work, we highlight an unforeseen behavior of the expressivity of Parameterized Quantum Circuits (PQCs) for machine learning. A large class of these models, seen as Fourier…
Trainability and Expressivity of Hamming-Weight Preserving Quantum Circuits for Machine Learning
Léo Monbroussou, Eliott Z. Mamon, Jonas Landman +3
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 researc…