  Much attention has been paid to dynamical simulation and quantum machine
learning (QML) independently as applications for quantum advantage, while the
possibility of using QML to enhance dynamical simulations has not been
thoroughly investigated. Here we develop a framework for using QML methods to
simulate quantum dynamics on near-term quantum hardware. We use generalization
bounds, which bound the error a machine learning model makes on unseen data, to
rigorously analyze the training data requirements of an algorithm within this
framework. This provides a guarantee that our algorithm is resource-efficient,
both in terms of qubit and data requirements. Our numerics exhibit efficient
scaling with problem size, and we simulate 20 times longer than Trotterization
on IBMQ-Bogota.
