Concept learning of parameterized quantum models from limited measurements

Understand

Classical learning of the expectation values of observables for quantum states is a natural variant of learning quantum states or channels.

• While learning-theoretic frameworks establish the sample complexity and the number of measurement shots per sample required for learning such statistical quantities, the interplay between these two variables has not been adequately quantified before.
• In this work, we take the probabilistic nature of quantum measurements into account in classical modelling and discuss these quantities under a single unified learning framework.
• We provide provable guarantees for learning parameterized quantum models that also quantify the asymmetrical effects and interplay of the two variables on the performance of learning algorithms.