On the Expressive Power of the Transverse-Field Ising Model for Graph Learning

arXiv:2608.17750 · quant-ph · Submitted 2026-08-18 · Read on arXiv

quant-ph

Submitted: 2026-08-18

Updated: 2026-09-17

Code: https://github.com/pasqal-io/emulators

Terminology

• Transformer — 24× in this paper · explained in 2 episodes
• Embeddings — 16× in this paper · explained in 2 episodes
• Alignment — 13× in this paper · explained in 1 episode
• Benchmark — 9× in this paper · explained in 2 episodes
• Ablation — 8× in this paper · explained in 1 episode
• Graph Edit Distance — 7× in this paper
• Attention Mechanism — 6× in this paper · explained in 3 episodes
• Hilbert Space — 4× in this paper · explained in 2 episodes
• Aggregation — 3× in this paper · explained in 1 episode
• GNNs — 3× in this paper · explained in 2 episodes
• International Conference on Machine Learning — 3× in this paper
• Ablation study — 2× in this paper · explained in 1 episode
• Entanglement — 2× in this paper · explained in 4 episodes
• Generalization — 2× in this paper · explained in 13 episodes
• International Conference on Learning Representations — 2× in this paper
• Regularization — 2× in this paper · explained in 3 episodes
• Representation Learning — 2× in this paper · explained in 2 episodes
• Robustness — 2× in this paper · explained in 5 episodes
• Adaptation — 1× in this paper · explained in 1 episode
• Adjacency Matrix — 1× in this paper · explained in 1 episode
• Benchmarking — 1× in this paper · explained in 3 episodes
• Consistency — 1× in this paper · explained in 3 episodes
• Distillation — 1× in this paper · explained in 2 episodes
• Hypergraph — 1× in this paper · explained in 2 episodes
• Knowledge Distillation — 1× in this paper · explained in 11 episodes
• Latent Space — 1× in this paper · explained in 7 episodes
• Theoretical Guarantees — 1× in this paper · explained in 2 episodes
• Trace — 1× in this paper · explained in 2 episodes
• Transformer Architecture — 1× in this paper · explained in 2 episodes
• Transformer Models — 1× in this paper · explained in 2 episodes

Sources

• Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gauges
• A Generalization of Transformer Networks to Graphs
• Multiparticle quantum walks for distinguishing hard graphs
• Quantum Optimization for Maximum Independent Set Using Rydberg Atom Arrays
• Learning Latent Permutations with Gumbel-Sinkhorn Networks

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