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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…
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…
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…