Resolving the locations and discriminating the spin states of individual
trapped ions with high fidelity is critical for a large class of applications
in quantum computing, simulation, and sensing. We report on a method for
high-fidelity state discrimination in large two-dimensional (2D) crystals with
over 100 trapped ions in a single trapping region, combining a novel hardware
detector and an artificial neural network. A high-data-rate, spatially
resolving, single-photon sensitive timestamping detector performs efficient
single-shot detection of 2D crystals in a Penning trap, exhibiting rotation at
about 25kHz. We then train an artificial neural network to process
the fluorescence photon data in the rest frame of the rotating crystal in order
to identify ion locations with a precision of  90%, accounting for
substantial illumination inhomogeneity across the crystal. Finally, employing a
time-binned state detection method, we arrive at an average spin-state
detection fidelity of 94(1)%. This technique can be used to analyze spatial
and temporal correlations in arrays of hundreds of trapped-ion qubits.Comment: 7 pages, 4 figure