Related papers: Efficient site-resolved imaging and spin-state detection in dynamic two-dimensional ion crystals

Efficient site-resolved imaging and spin-state detection in dynamic two-dimensional ion crystals

URL: http://arxiv.org/abs/2303.10801v4
Date: Mon, 16 Sep 2024 05:22:31 GMT
Title: Efficient site-resolved imaging and spin-state detection in dynamic two-dimensional ion crystals
Authors: Robert N. Wolf, Joseph H. Pham, Julian Y. Z. Jee, Alexander Rischka, Michael J. Biercuk,
Abstract summary: 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.
Score: 39.58317527488534
License: http://creativecommons.org/licenses/by-nc-nd/4.0/
Abstract: 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 $25\,\mathrm{kHz}$. 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.

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