An Algorithm for Fast Assembling Large-Scale Defect-Free Atom Arrays
Tao Zhang, Xiaodi Li, Hui Zhai, Linghui Chen

TL;DR
This paper introduces a novel algorithmic framework combining machine learning and optical potential generation to rapidly assemble large-scale, defect-free atom arrays for quantum computing, overcoming key computational and hardware challenges.
Contribution
The paper presents a unified approach with a graph neural network-based path-planning module and a phase-aware algorithm for fast optical potential generation, enabling large atom arrays.
Findings
Inference time for path planning is ~5 ms, nearly size-independent.
Potential generation takes about 0.5 ms, faster than current SLM refresh rates.
Enables assembly of 10,000-qubit atom arrays within atom lifetime constraints.
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)…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
