We present a quantum-inspired classification framework designed to identify correlation structures: product, separable, and entangled, in random mixed quantum states. Building on previous work where the Pretty-Good-Measurement (PGM) classifier demonstrated a competitive performance on pure-state ensembles, we extend this method to the more challenging domain of mixed states. We apply our quantum-inspired classifier to randomly generated ensembles of two- and three-qubit mixed states, encompassing all possible varieties of subsystem correlations while ensuring statistical neutrality. The results indicate that learning architectures inspired by quantum state discrimination can offer scalable and physically grounded tools for the characterization of entanglement and separability even in the mixed-state regime.
Sergioli et al. (2026) studied this question.