Realistic Neutral Atom Image Simulation

• URL: http://arxiv.org/abs/2310.02836v1
• Date: Wed, 4 Oct 2023 14:02:18 GMT
• Title: Realistic Neutral Atom Image Simulation
• Authors: Jonas Winklmann, Dimitrios Tsevas, Martin Schulz
• Abstract summary: Bottom-up simulator capable of generating sample images of neutral atom experiments from a description of the actual state in the simulated system. Use cases include the creation of exemplary images for demonstration purposes, fast training iterations for deconvolution algorithms, and generation of labeled data for machine-learning atom detection approaches.
• Score: 1.3220067655295737
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Neutral atom quantum computers require accurate single atom detection for the preparation and readout of their qubits. This is usually done using fluorescence imaging. The occupancy of an atom site in these images is often somewhat ambiguous due to the stochastic nature of the imaging process. Further, the lack of ground truth makes it difficult to rate the accuracy of reconstruction algorithms. We introduce a bottom-up simulator that is capable of generating sample images of neutral atom experiments from a description of the actual state in the simulated system. Possible use cases include the creation of exemplary images for demonstration purposes, fast training iterations for deconvolution algorithms, and generation of labeled data for machine-learning-based atom detection approaches. The implementation is available through our GitHub as a C library or wrapped Python package. We show the modeled effects and implementation of the simulations at different stages of the imaging process. Not all real-world phenomena can be reproduced perfectly. The main discrepancies are that the simulator allows for only one characterization of optical aberrations across the whole image, supports only discrete atom locations, and does not model all effects of CMOS cameras perfectly. Nevertheless, our experiments show that the generated images closely match real-world pictures to the point that they are practically indistinguishable and can be used as labeled data for training the next generation of detection algorithms.

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