1 citations · 2 across the 6 of their papers we have counts for
6 papers
On the Expressive Power of the Transverse-Field Ising Model for Graph Learning
Mehdi Djellabi, Louis-Paul Henry
We study the quantum evolution induced by graph-indexed Ising Hamiltonians as a source of structural signal for graph learning. Graph automorphisms preserve symmetries of the Hamil…
Quantum Graph Attention Networks: Trainable Quantum Encoders for Inductive Graph Learning
Arthur M. Faria, Mehdi Djellabi, Igor O. Sokolov +1
We introduce Quantum Graph Attention Networks (QGATs) as trainable quantum encoders for inductive learning on graphs, extending the Quantum Graph Neural Networks (QGNN) framework.…
Attributed-graphs kernel implementation using local detuning of neutral-atoms Rydberg Hamiltonian
Mehdi Djellabi, Matthias Hecker, Shaheen Acheche
We extend the quantum-feature kernel framework, which relies on measurements of graph-dependent observables, along three directions. First, leveraging neutral-atom quantum processi…
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…
Graph Algorithms with Neutral Atom Quantum Processors
Constantin Dalyac, Lucas Leclerc, Louis Vignoli +9
Neutral atom technology has steadily demonstrated significant theoretical and experimental advancements, positioning itself as a front-runner platform for running quantum algorithm…
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…