Low-Latency FPGA Control System for Real-Time Neural Network Processing in CCD-Based Trapped-Ion Qubit Measurement

2025-12-17

AI Summary

Real-time neural-network-based measurement of trapped-ion qubits has not been systematically investigated, particularly regarding latency constraints. Method: This work introduces an ultra-low-latency FPGA control system that establishes a direct hardware interface (FPGA–EMCCD hard connection) between an electron-multiplying CCD (EMCCD) camera and a neural-network inference unit—eliminating conventional interface and buffering overheads. A lightweight Vision Transformer (ViT) and multilayer perceptron (MLP) are deployed on the FPGA, enabling on-chip inference with nanosecond- to microsecond-scale latency. Clock-cycle-level signal analysis identifies CameraLink as the critical bottleneck. Contribution/Results: Compared to threshold-based detection, measurement errors for single and three-qubit states are reduced by 1.8×–7.6×. End-to-end single-shot measurement throughput exceeds GPU-based implementations by over two orders of magnitude, significantly advancing high-speed, high-fidelity readout in trapped-ion quantum computing.

Technology Category

• Machine Learning: Hardware-aware ML
• Cognitive Modeling & Cognitive Systems: Neural Spike Coding
• Computer Vision: Learning & Optimization for CV

Application Category

• Security and Privacy: Large-scale security measurements
• Systems and Infrastructure for Web, Mobile and WoT: Web performance, measurement, and characterization
• Economics, Online Markets and Human Computation: LLM based quality controls for crowd work

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.

Problem

• Benchmarks DNN latency on FPGAs and GPUs for qubit measurement
• Eliminates buffering overhead by direct EMCCD-FPGA interfacing
• Reveals inefficiencies in Cameralink data transmission for optimization

Innovation

• FPGA directly interfaces EMCCD for low-latency neural processing
• Benchmarks DNN models like MLP and ViT on FPGA and GPU
• Achieves nanosecond inference and 100x speedup over GPU baseline