Quick Navigation
Topics
Trapped Ion Quantum Computing
Efficient Image Reconstruction Architecture for Neutral Atom Quantum Computing
arXiv
Authors: Jonas Winklmann, Yian Yu, Xiaorang Guo, Korbinian Staudacher, Martin Schulz
Year
2026
Paper ID
22414
Status
Preprint
Abstract Read
~2 min
Abstract Words
175
Citations
N/A
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 256times256-pixel image representing a 10times10 atom array in just 115 μs on a Xilinx UltraScale+ FPGA. Compared to the original CPU baseline and our optimized CPU version, we achieve about 34.9times and 6.3times speedup of the reconstruction time, respectively. Moreover, this work also contributes to the ongoing efforts toward fully integrated FPGA-based control systems for NAQCs.
Why This Paper Matters
- This paper contributes to the Trapped-Ion Quantum Computing research area in the Quantum Articles archive.
- It adds a 2026 reference point for readers tracking recent quantum research.
- In recent years, neutral atom quantum computers (NAQCs) have attracted a lot of attention, primarily due to their long coherence times and good scalability.
Paper Tools
Become a member to use research tools
Sign in to open papers, visit source links, share, cite, compare, copy DOI links, request category corrections, and build your reading list.
Show Paper arXiv Publisher Share
Cite This Paper
Copy URL
Compare
Copy DOI Add to Reading List
Category Correction Request
Category Correction Request
Help us improve classification quality by proposing a better category. Every request is reviewed by an admin.
Sign in to submit a category correction request for this paper.
Log In to SubmitReferences & Citation Signals
Community Reactions
Quick sentiment from readers on this paper.
Score:
0
Likes: 0
Dislikes: 0
Sign in to react to this paper.
Discussion & Reviews (Moderated)
Average Rating: 0.0 / 5 (0 ratings)
No written reviews yet.