arXiv:2606. 15983v1 Announce Type: cross Abstract: Recent theoretical progress has established conditions under which machine learning models can efficiently predict ground-state properties of gapped local Hamiltonians when trained on quantum-generated data.
By Ben Jaderberg, Freya Shah, Minjun Jeon, M. Emre Sahin, Christa Zoufal, Kunal Sharma
arXiv:2607. 13847v1 Announce Type: cross Abstract: Many datasets encountered across a wide range of domains possess rich geometric and topological structure that is difficult to capture using conventional vector-based representations.
By Adam Weso{\l}owski, Dimitrios Thanos, Daniel Leykam, Lirand\"e Pira
TetrisCNN is a convolutional neural network that uses parallel branches of differently shaped filters to learn sparse, interpretable latent representations directly in terms of spin correlators. Applied to experimental snapshots from two-dimensional Ising and XY quantum simulators measured in multiple bases, the network detects phase transitions and crossovers while expressing its decision boundaries as symbolic formulas built from experimentally measurable spin correlators. This approach bridges the gap between black‑box neural network methods and physically interpretable models, enabling automated detection of phases of matter from realistic, noisy experimental data.
By Kacper Cybi\'nski, Bj\"orn van Zwol, James Enouen, Guillaume Bornet, Thierry Lahaye, Antoine Browaeys, Antoine Georges, Anna Dawid
The paper introduces a quantum method for extracting spectral features from the density of states (DOS) of a problem-dependent Hamiltonian, applied to signed graphs represented as Ising models. It demonstrates that standardized moments of the Ising DOS count signed closed walks, are switching‑invariant, and size‑free, enabling accurate learning of the frustration index with a mean error of 0.4 on 140,000 labeled graphs. The authors propose DOS‑QPE, a phase estimation technique that requires far fewer shots than traditional methods, and highlight potential applications in social network analysis, spin‑glass studies, correlation clustering, and protein‑interaction networks.
The paper introduces a quantum method for extracting spectral features from the density of states (DOS) of a problem-dependent Hamiltonian, applied to signed graphs represented as Ising models. Using standardized moments of the Ising DOS as features, the authors demonstrate that these moments count signed closed walks, are switching‑invariant, and size‑free. On a benchmark of 140,000 labeled graphs, the exact DOS predicts the frustration index exactly, while five moments achieve a mean error of 0.4, and a new DOS‑QPE protocol offers efficient sampling with far fewer shots than classical trace‑sampling methods.
By Stefano Scali, Oleksandr Kyriienko
TetrisCNN is a convolutional neural network that uses parallel branches of differently shaped filters to learn sparse, interpretable latent representations directly from spin correlators. Applied to experimental snapshots of two-dimensional Ising and XY quantum simulators, it detects phase transitions and crossovers while expressing its decision boundaries as symbolic formulas built from measurable spin correlators. This approach bridges the gap between black‑box neural networks and physically interpretable models, enabling automated discovery of new phases of matter from realistic, noisy experimental data.