6 citations · 10 across the 4 of their papers we have counts for
4 papers · 1 filter
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…