← Back to all news
arXiv Machine Learning By Stefano Scali, Oleksandr Kyriienko

Learning structural balance of graphs from quantum spectral features

Read the original on arXiv Machine Learning →

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.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

Related stories

Hugging Face Trending Papers
Sep 10

Learning structural balance of graphs from quantum spectral features

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.

benchmarks
More like this →
arXiv Machine Learning
Jul 23

Edge-Local and Qubit-Efficient Quantum Graph Learning for the NISQ Era

arXiv:2602. 16018v2 Announce Type: replace-cross Abstract: Graph neural networks (GNNs) are a powerful framework for learning representations from graph-structured data, but their direct implementation on near-term quantum hardware remains challenging due to circuit depth, multi-qubit interactions, and qubit scalability constraints.

By Armin Ahmadkhaniha, Jake Doliskani
More like this →
arXiv Machine Learning
Jul 14

Learning Topological Quantum Phases from Limited Subsystems

arXiv:2607. 10656v1 Announce Type: cross Abstract: Characterizing quantum topological phases requires measuring non-local string order parameters, demanding access to the full system, which is often experimentally unfeasible.

By Mehran Khosrojerdi, Sougato Bose, Alessandro Cuccoli, Paola Verrucchi, Abolfazl Bayat, Leonardo Banchi
benchmarks
More like this →