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

TL;DR

This paper benchmarks low-latency neural network inference on FPGAs for real-time qubit measurement, demonstrating significant speedups over GPU systems and identifying interface bottlenecks for further optimization.

Contribution

It introduces an FPGA-based control system for real-time neural network processing in trapped-ion qubit measurement, achieving nanosecond to microsecond inference latencies and highlighting hardware bottlenecks.

Findings

DNNs improve measurement fidelity by up to 7.6x

FPGA inference latency is in the nanosecond to microsecond range

Complete measurement process is over 100x faster on FPGA than GPU

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.