  We propose a series of data-centric heuristics for improving the performance
of machine learning systems when applied to problems in quantum information
science. In particular, we consider how systematic engineering of training sets
can significantly enhance the accuracy of pre-trained neural networks used for
quantum state reconstruction without altering the underlying architecture. We
find that it is not always optimal to engineer training sets to exactly match
the expected distribution of a target scenario, and instead, performance can be
further improved by biasing the training set to be slightly more mixed than the
target. This is due to the heterogeneity in the number of free variables
required to describe states of different purity, and as a result, overall
accuracy of the network improves when training sets of a fixed size focus on
states with the least constrained free variables. For further clarity, we also
include a "toy model" demonstration of how spurious correlations can
inadvertently enter synthetic data sets used for training, how the performance
of systems trained with these correlations can degrade dramatically, and how
the inclusion of even relatively few counterexamples can effectively remedy
such problems.

Quantum Physics;Artificial Intelligence;Machine Learning