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High-fidelity quantum dynamics emulators can be used to predict the timeevolution of complex physical systems. Here, we introduce an efficient trainingframework for constructing machine learning-based emulators. Our approach isbased on the idea of knowledge distillation and uses elements of curriculumlearning. It works by constructing a set of simple, but rich-in-physicstraining examples (a curriculum). These examples are used by the emulator tolearn the general rules describing the time evolution of a quantum system(knowledge distillation). The goal is not only to obtain high-qualitypredictions, but also to examine the process of how the emulator learns thephysics of the underlying problem. This allows us to discover new facts aboutthe physical system, detect symmetries, and measure relative importance of thecontributing physical processes. We illustrate this approach by training anartificial neural network to predict the time evolution of quantum wavepackages propagating through a potential landscape. We focus on the question ofhow the emulator learns the rules of quantum dynamics from the curriculum ofsimple training examples and to which extent it can generalize the acquiredknowledge to solve more challenging cases.

Emulating Quantum Dynamics with Neural Networks via Knowledge Distillation