  The efficient validation of quantum devices is critical for emerging
technological applications. In a wide class of use-cases the precise
engineering of a Hamiltonian is required both for the implementation of
gate-based quantum information processing as well as for reliable quantum
memories. Inferring the experimentally realized Hamiltonian through a scalable
number of measurements constitutes the challenging task of Hamiltonian
learning. In particular, assessing the quality of the implementation of
topological codes is essential for quantum error correction. Here, we introduce
a neural net based approach to this challenge. We capitalize on a family of
exactly solvable models to train our algorithm and generalize to a broad class
of experimentally relevant sources of errors. We discuss how our algorithm
scales with system size and analyze its resilience towards various noise
sources.
