An Algorithm for Fast Assembling Large-Scale Defect-Free Atom Arrays
2026-04-09 • Machine Learning
Machine Learning
AI summaryⓘ
The authors address the challenge of organizing about 10,000 atoms precisely for quantum computers using optical tweezers. They create a two-part algorithm: one part quickly plans paths for moving atoms using a neural network, and the other efficiently shapes the light needed to move them. Their method works fast enough to arrange all atoms before they drift away, which was previously very hard. This progress could help build larger, practical quantum computers.
quantum computingatom arraysoptical tweezerspath planninggraph neural networkauction algorithmspatial light modulatorGerchberg-Saxton algorithmquantum qubitsvacuum lifetime
Authors
Tao Zhang, Xiaodi Li, Hui Zhai, Linghui Chen
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.