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 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 success rate 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(2)%. This technique can be used to analyze spatial and temporal correlations in arrays of hundreds of trapped-ion qubits.
@article{arxiv.2303.10801,
title = {Efficient site-resolved imaging and spin-state detection in dynamic two-dimensional ion crystals},
author = {Robert N. Wolf and Joseph H. Pham and Julian Y. Z. Jee and Alexander Rischka and Michael J. Biercuk},
journal= {arXiv preprint arXiv:2303.10801},
year = {2024}
}