  We determine the resource scaling of machine learning-based quantum state
reconstruction methods, in terms of both inference and training, for systems of
up to four qubits. Further, we examine system performance in the low-count
regime, likely to be encountered in the tomography of high-dimensional systems.
Finally, we implement our quantum state reconstruction method on a IBM Q
quantum computer and confirm our results.
