  High-fidelity measurements are important for the physical implementation of
quantum information protocols. Current methods for classifying measurement
trajectories in superconducting qubit systems produce fidelities that are
systematically lower than those predicted by experimental parameters. Here, we
place current classification methods within the framework of machine learning
algorithms and improve on them by investigating more sophisticated ML
approaches. We find that non-linear algorithms and clustering methods produce
significantly higher assignment fidelities that help close the gap to the
fidelity achievable under ideal noise conditions. Clustering methods group
trajectories into natural subsets within the data, which allows for the
diagnosis of specific systematic errors. We find large clusters in the data
associated with relaxation processes and show these are the main source of
discrepancy between our experimental and achievable fidelities. These error
diagnosis techniques help provide a concrete path forward to improve qubit
measurements.
