Algorithmic models in quantum mechanics

We study classical and quantum learning algorithms with access to data produced by a quantum process. First, we consider the problem of learning quantum states and, in the framework of the probably approximately correct (PAC) model, prove that stabiliser states are efficiently learnable. Second, we introduce a generative model based on artificial neural networks capable of finding efficient representations of quantum states and assess its performance on states with varying levels of complexity. Third, we discuss the time complexity of classical and quantum learning algorithms and prove that Boolean functions in disjunctive normal form are efficiently quantum PAC learnable under product distributions.