233 citations · 296 across the 10 of their papers we have counts for
10 papers
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…
Quantum Positional Encodings for Graph Neural Networks
Slimane Thabet, Mehdi Djellabi, Igor Sokolov +3
In this work, we propose novel families of positional encodings tailored to graph neural networks obtained with quantum computers. These encodings leverage the long-range correlati…
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…
Enhancing Graph Neural Networks with Quantum Computed Encodings
Slimane Thabet, Romain Fouilland, Mehdi Djellabi +4
Transformers are increasingly employed for graph data, demonstrating competitive performance in diverse tasks. To incorporate graph information into these models, it is essential t…
Quantum Feature Maps for Graph Machine Learning on a Neutral Atom Quantum Processor
Boris Albrecht, Constantin Dalyac, Lucas Leclerc +10
Using a quantum processor to embed and process classical data enables the generation of correlations between variables that are inefficient to represent through classical computati…
Classically Approximating Variational Quantum Machine Learning with Random Fourier Features
Jonas Landman, Slimane Thabet, Constantin Dalyac +2
Many applications of quantum computing in the near term rely on variational quantum circuits (VQCs). They have been showcased as a promising model for reaching a quantum advantage…
Extending Graph Transformers with Quantum Computed Aggregation
Slimane Thabet, Romain Fouilland, Loic Henriet
Recently, efforts have been made in the community to design new Graph Neural Networks (GNN), as limitations of Message Passing Neural Networks became more apparent. This led to the…
Quantum evolution kernel : Machine learning on graphs with programmable arrays of qubits
Louis-Paul Henry, Slimane Thabet, Constantin Dalyac +1
The rapid development of reliable Quantum Processing Units (QPU) opens up novel computational opportunities for machine learning. Here, we introduce a procedure for measuring the s…
Laplacian Eigenmaps with variational circuits: a quantum embedding of graph data
Slimane Thabet, Jean-Francois Hullo
With the development of quantum algorithms, high-cost computations are being scrutinized in the hope of a quantum advantage. While graphs offer a convenient framework for multiple…
PennyLane: Automatic differentiation of hybrid quantum-classical computations
Ville Bergholm, Josh Izaac, Maria Schuld +65
PennyLane is a Python 3 software framework for differentiable programming of quantum computers. The library provides a unified architecture for near-term quantum computing devices,…