Inferring interpretable dynamical generators of local quantum observables from projective measurements through machine learning

Reconstructing the effective equation of motion for the time evolution of a subset of degrees of freedom of a larger system remains a problem of interest in quantum physics. Many methods have been developed, but they either rely on an ad hoc ansatz, demand data that is not experimentally accessible, or lack physical interpretability. The authors employ machine-learning methods to infer the effective dynamical generator from a noisy, finite set of local measurements. Their method yields interpretable results that may be used to infer noise models on quantum simulators, or to study thermalization dynamics in quantum many-body systems.