Efficient preparation of 2D defect-free atom arrays with near-fewest sorting-atom moves

TL;DR

This paper introduces a new heuristic clustering algorithm for efficiently preparing large, defect-free 2D atom arrays with minimal sorting moves, demonstrated experimentally with high filling fractions.

Contribution

The paper presents the heuristic cluster algorithm (HCA), a novel sorting method that reduces sorting moves and scales effectively for large atom arrays in quantum applications.

Findings

Achieved 98.4% filling fraction in a 5x6 atom array.

HCA minimizes sorting moves, making array scaling feasible.

Demonstrated experimental realization of defect-free atom arrays.

Abstract

Sorting atoms stochastically loaded in optical tweezer arrays via an auxiliary mobile tweezer is an efficient approach to preparing intermediate-scale defect-free atom arrays in arbitrary geometries. However, high filling fraction of atom-by-atom assemblers is impeded by redundant sorting moves with imperfect atom transport, especially for scaling the system size to larger atom numbers. Here, we propose a new sorting algorithm (heuristic cluster algorithm, HCA) which provides near-fewest moves in our tailored atom assembler scheme and experimentally demonstrate a $5\times6$ defect-free atom array with 98.4(7)$\%$ filling fraction for one rearrangement cycle. The feature of HCA that the number of moves $N_{m}\approx N$ ($N$ is the number of defect sites to be filled) makes the filling fraction uniform as the size of atom assembler enlarged. Our method is essential to scale hundreds of assembled atoms for bottom-up quantum computation, quantum simulation and precision measurement.