On the Expressive Power of the Transverse-Field Ising Model for Graph Learning
QDAGer is introduced, a quantum-inspired graph-pair Transformer that injects quantum-dynamical features from time series of node occupations and connected two-point correlators directly into the attention mechanism, and applies it to learning Graph Edit Distance, an NP-hard similarity measure.