Machine-learning certification of multipartite entanglement for noisy quantum hardware - Andreas J C Fuchs; Eric Brunner; Jiheon Seong; Hyeokjea Kwon; Seungchan Seo; Joonwoo Bae; Andreas Buchleitner; Edoardo G Carnio (New Journal of Physics, 2025)
Entanglement is a fundamental aspect of quantum physics, both conceptually and for its many applications. Classifying an arbitrary multipartite state as entangled or separable—a task referred to as the separability problem—poses a significant challenge, since a state can be entangled with respect to many different of its partitions. We develop a certification pipeline that feeds the statistics of random local measurements into a non-linear dimensionality reduction algorithm, to determine with respect to which partitions a given quantum state is entangled. After training a model on randomly generated quantum states, entangled in different partitions and of varying purity, we verify the accuracy of its predictions on simulated test data, and finally apply it to states prepared on IBM quantum computing hardware.