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A quantum approach to spectral feature extraction from the density of states (DOS) of a problem-dependent Hamiltonian is developed, and applied to machine learning on signed graphs, and opens routes towards quantum applications in social network balance analysis, spin-glass studies, correlation clustering, and protein-interaction networks.