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Generative modeling using samples drawn from the probability distributionconstitutes a powerful approach for unsupervised machine learning. Quantummechanical systems can produce probability distributions that exhibit quantumcorrelations which are difficult to capture using classical models. We showtheoretically that such quantum correlations provide a powerful resource forgenerative modeling. In particular, we provide an unconditional proof ofseparation in expressive power between a class of widely-used generativemodels, known as Bayesian networks, and its minimal quantum extension. We showthat this expressivity advantage is associated with quantum nonlocality andquantum contextuality. Furthermore, we numerically test this separation onstandard machine learning data sets and show that it holds for practicalproblems. The possibility of quantum advantage demonstrated in this work notonly sheds light on the design of useful quantum machine learning protocols butalso provides inspiration to draw on ideas from quantum foundations to improvepurely classical algorithms.

Enhancing Generative Models via Quantum Correlations