Low-Latency FPGA Control System for Real-Time Neural Network Processing in CCD-Based Trapped-Ion Qubit Measurement
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Quantum Physics arXiv:2512.15838 (quant-ph) [Submitted on 17 Dec 2025] Title:Low-Latency FPGA Control System for Real-Time Neural Network Processing in CCD-Based Trapped-Ion Qubit Measurement Authors:Binglei Lou, Gautham Duddi Krishnaswaroop, Filip Wojcicki, Ruilin Wu, Richard Rademacher, Zhiqiang Que, Wayne Luk, Philip H.W. Leong View a PDF of the paper titled Low-Latency FPGA Control System for Real-Time Neural Network Processing in CCD-Based Trapped-Ion Qubit Measurement, by Binglei Lou and 7 other authors View PDF HTML (experimental) Abstract:Accurate and low-latency qubit state measurement is critical for trapped-ion quantum computing. While deep neural networks (DNNs) have been integrated to enhance detection fidelity, their latency performance on specific hardware platforms remains underexplored. This work benchmarks the latency of DNN-based qubit detection on field-programmable gate arrays (FPGAs) and graphics processing units (GPUs). The FPGA solution directly interfaces an electron-multiplying charge-coupled device (EMCCD) with the subsequent data processing logic, eliminating buffering and interface overheads. As a baseline, the GPU-based system employs a high-speed PCIe image grabber for image input and I/O card for state output. We deploy Multilayer Perceptron (MLP) and Vision Transformer (ViT) models on hardware to evaluate measurement performance. Compared to conventional thresholding, DNNs reduce the mean measurement fidelity (MMF) error by factors of 1.8-2.5x (one-qubit case) and 4.2-7.6x (three-qubit case). FPGA-based MLP and ViT achieve nanosecond- and microsecond-scale inference latencies, while the complete single-shot measurement process achieves over 100x speedup compared to the GPU implementation. Additionally, clock-cycle-level signal analysis reveals inefficiencies in EMCCD data transmission via Cameralink, suggesting that optimizing this interface could further leverage the advantages of ultra-low-latency DNN inference, guiding the development of next-generation qubit detection systems. Subjects: Quantum Physics (quant-ph); Hardware Architecture (cs.AR) Cite as: arXiv:2512.15838 [quant-ph] (or arXiv:2512.15838v1 [quant-ph] for this version) https://doi.org/10.48550/arXiv.2512.15838 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Binglei Lou [view email] [v1] Wed, 17 Dec 2025 18:34:00 UTC (2,983 KB) Full-text links: Access Paper: View a PDF of the paper titled Low-Latency FPGA Control System for Real-Time Neural Network Processing in CCD-Based Trapped-Ion Qubit Measurement, by Binglei Lou and 7 other authorsView PDFHTML (experimental)TeX Source view license Current browse context: quant-ph new | recent | 2025-12 Change to browse by: cs cs.AR References & Citations INSPIRE HEP NASA ADSGoogle Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... Data provided by: Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv (What is alphaXiv?) Links to Code Toggle CatalyzeX Code Finder for Papers (What is CatalyzeX?) DagsHub Toggle DagsHub (What is DagsHub?) GotitPub Toggle Gotit.pub (What is GotitPub?) Huggingface Toggle Hugging Face (What is Huggingface?) Links to Code Toggle Papers with Code (What is Papers with Code?) ScienceCast Toggle ScienceCast (What is ScienceCast?) Demos Demos Replicate Toggle Replicate (What is Replicate?) Spaces Toggle Hugging Face Spaces (What is Spaces?) Spaces Toggle TXYZ.AI (What is TXYZ.AI?) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower (What are Influence Flowers?) Core recommender toggle CORE Recommender (What is CORE?) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs. Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)