arXiv:2611.08821v1 Announce Type: new Abstract: Structural descriptors provide graph neural networks (GNNs) with positional and topological inductive biases. However, widely used classical encodings based on random walks or Laplacian spectra emphasize specific structural statistics of a graph, leaving room for alternative descriptors. Neutral-atom quantum processing units (QPUs) can realize graph-dependent Rydberg Hamiltonians, allowing graph topology to be probed through dynamical correlation measurements.
Quantum Research · 2026-11-09
Neutral-atom quantum features as complementary structural encodings for graph learning
arXiv:2611.08821v1 Announce Type: new Abstract: Structural descriptors provide graph neural networks (GNNs) with positional and topological inductive biases. However, widely used classical encodings based on random walks or Laplacian spectra emphasize specific structural statistics of a graph, leaving room for alternative descriptors.
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Original source: arXiv Quantum ↗
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