An Algorithm for Fast Assembling Large-Scale Defect-Free Atom Arrays

2026-04-09

AI Summary

This work addresses two major bottlenecks in assembling large-scale defect-free atomic arrays: the high computational complexity of path planning and the prolonged time required for optical tweezer potential generation. To overcome these challenges, the authors propose a unified algorithmic framework that integrates graph neural networks with an enhanced auction decoder for efficient atom rearrangement path planning, alongside a phase- and contour-aware weighted Gerchberg–Saxton algorithm for rapid potential field synthesis. This approach drastically reduces computational overhead, achieving stable path planning inference times of approximately 5 ms and potential field frame generation in just 0.5 ms—significantly faster than the atomic vacuum lifetime. Consequently, the method enables rapid construction of defect-free atomic arrays at scales exceeding ten thousand atoms.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Search and Optimization: Algorithm Configuration
• Intelligent Robots: Motion and Path Planning

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web
• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments

Abstract

It is widely believed that tens of thousands of physical qubits are needed to build a practically useful quantum computer. Atom arrays formed by optical tweezers are among the most promising platforms for achieving this goal, owing to the excellent scalability and mobility of atomic qubits. However, assembling a defect-free atom array with ~ 10^4 qubits remains algorithmically challenging, alongside other hardware limitations. This is due to the computationally hard path-planning problems and the time-consuming generation of suffciently smooth trajectories for optical tweezer potentials by spatial light modulators (SLM). Here, we present a unified framework comprising two innovative components to fully address these algorithmic challenges: (1) a path-planning module that employs a supervised learning approach using a graph neural network combined with a modified auction decoder, and (2) a potential-generation module called the phase and profile-aware Weighted Gerchberg-Saxton algorithm. The inference time for the first module is nearly a size-independent constant overhead of ~ 5 ms, and the second module generates a potential frame with about 0.5 ms, a timescale shorter than the current commercial SLM refresh time. Altogether, our algorithm enables the assembly of an atom array with 10^4 qubits on a timescale much shorter than the typical vacuum lifetime of the trapped atoms.

Problem

• atom arrays
• defect-free assembly
• path planning
• optical tweezers
• quantum computing

Innovation

• graph neural network
• optical tweezer
• Gerchberg-Saxton algorithm
• atom array assembly
• quantum computing