Learning structural balance of graphs from quantum spectral features
We develop a quantum approach to spectral feature extraction from the density of states (DOS) of a problem-dependent Hamiltonian, and apply it to machine learning on signed graphs. We propose to embed a signed graph as an Ising model instance with positive and negative interactions, and use the standardized moments of the Ising DOS as features for learning. We show that these moments count signed closed walks, are switching-invariant, and are size-free by construction. As a benchmark, we target learning the frustration index, an NP-hard measure of structural balance that can be labeled exactly at moderate size. At zero field, the models can be sampled classically, allowing the quantum ext...
| Authors | Stefano Scali, Oleksandr Kyriienko |
|---|---|
| Primary category | quant-ph |
| Published | 10 September 2026 |
| Last updated at source | 10 September 2026 |
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