Efficient Image Reconstruction Architecture for Neutral Atom Quantum Computing

2026-03-03

AI Summary

Fluorescence imaging and state detection in neutral-atom quantum computers suffer from excessive latency, posing a major bottleneck in system control. This work proposes a highly parallel atomic detection acceleration architecture tailored for optical tweezer–based neutral-atom platforms, integrating algorithmic optimization with deep FPGA hardware co-design to achieve microsecond-scale image reconstruction for the first time. Implemented on a Xilinx UltraScale+ platform, the architecture combines parallel image processing with hardware acceleration techniques, reconstructing a 256×256-pixel image in just 115 microseconds—yielding speedups of 34.9× and 6.3× over the original and optimized CPU implementations, respectively. This advancement significantly advances the practicality of fully integrated FPGA-based control systems for scalable neutral-atom quantum computing.

Technology Category

• Computer Vision: Learning & Optimization for CV
• Machine Learning: Hardware-aware ML
• Search and Optimization: Learning to Search

Application Category

• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines
• Systems and Infrastructure for Web, Mobile and WoT: Federated Web and WoT systems, including distributed, federated and edge-based data processing
• Economics, Online Markets and Human Computation: Economics and fairness of platforms and recommendation systems

Abstract

In recent years, neutral atom quantum computers (NAQCs) have attracted a lot of attention, primarily due to their long coherence times and good scalability. One of their main drawbacks is their comparatively time-consuming control overhead, with one of the main contributing procedures being the detection of individual atoms and measurement of their states, each occurring at least once per compute cycle and requiring fluorescence imaging and subsequent image analysis. To reduce the required time budget, we propose a highly-parallel atom-detection accelerator for tweezer-based NAQCs. Building on an existing solution, our design combines algorithm-level optimization with a field-programmable gate array (FPGA) implementation to maximize parallelism and reduce the run time of the image analysis process. Our design can analyze a 256$\times$256-pixel image representing a 10$\times$10 atom array in just 115 $μ$s on a Xilinx UltraScale+ FPGA. Compared to the original CPU baseline and our optimized CPU version, we achieve about 34.9$\times$ and 6.3$\times$ speedup of the reconstruction time, respectively. Moreover, this work also contributes to the ongoing efforts toward fully integrated FPGA-based control systems for NAQCs.

Problem

• neutral atom quantum computing
• atom detection
• fluorescence imaging
• image reconstruction
• control overhead

Innovation

• neutral atom quantum computing
• image reconstruction
• FPGA acceleration
• parallel processing
• atom detection