  We determine the resource scaling of machine learning-based quantum state
reconstruction methods, in terms of inference and training, for systems of up
to four qubits when constrained to pure states. 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 an IBM Q quantum computer, and compare against both
unconstrained and constrained MLE state reconstruction.
