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arXiv:2511.16303

ATLAS: Efficient Atom Rearrangement for Defect-Free Neutral-Atom Quantum Arrays Under Transport Loss

Otto Savola, Alexandru Paler

quant-phSubmitted Nov 20, 2025• Announced Nov 21, 2025• 0 citations
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Abstract

Neutral-atom quantum computers encode qubits in individually trapped atoms arranged in optical lattices. Achieving defect-free atom configurations is essential for high-fidelity quantum gates and scalable error correction, yet stochastic loading and atom loss during rearrangement hinder reliable large-scale assembly. This work presents ATLAS, an open-source atom transport algorithm that efficiently converts a randomly loaded $W \times W$ lattice into a defect-free $L \times L$ subarray while accounting for realistic physical constraints, including finite acceleration, transfer time, and per-move loss probability. In the planning phase, optimal batches of parallel moves are computed on a lossless virtual array; during execution, these moves are replayed under probabilistic atom loss to maximize the expected number of retained atoms. Monte Carlo simulations across lattice sizes $W=10$--$100$, loading probabilities $p_{\mathrm{occ}}=0.5$--$0.9$, and loss rates $p_{\mathrm{loss}}=0$--$0.05$ demonstrate fill rates above $99\%$ within six iterations and over $90\%$ atom retention at low loss. The algorithm achieves sublinear move scaling ($\propto M^{0.55}$) and linear growth of required initial size with target dimension, outperforming prior methods in robustness and scalability -- offering a practical path toward larger neutral-atom quantum arrays.

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Neutral-atom quantum computers encode qubits in individually trapped atoms arranged in optical lattices. Achieving defect-free atom configurations is essential for high-fidelity quantum gates and scalable error correction, yet stochastic loading and atom loss during rearrangement hinder reliable large-scale assembly. This work presents ATLAS, an open-source atom transport algorithm that efficiently converts a randomly loaded W×WW \times WW×W lattice into a defect-free L×LL \times LL×L subarray while accounting for realistic physical constraints, including finite acceleration, transfer time, and per-move loss probability. In the planning phase, optimal batches of parallel moves are computed on a lossless virtual array; during execution, these moves are replayed under probabilistic atom loss to maximize the expected number of retained atoms. Monte Carlo simulations across lattice sizes W=10W=10W=10--100100100, loading probabilities pocc=0.5p_{\mathrm{occ}}=0.5pocc​=0.5--0.90.90.9, and loss rates ploss=0p_{\mathrm{loss}}=0ploss​=0--0.050.050.05 demonstrate fill rates above 99%99\%99% within six iterations and over 90%90\%90% atom retention at low loss. The algorithm achieves sublinear move scaling (∝M0.55\propto M^{0.55}∝M0.55) and linear growth of required initial size with target dimension, outperforming prior methods in robustness and scalability -- offering a practical path toward larger neutral-atom quantum arrays.
Neutral atoms have emerged as a promising technology for implementing quantum computers due to their scalability and long coherence times. However, the execution frequency of neutral atom quantum computers is constrained by image processing procedures, particularly the assembly of defect-free atom arrays, which is a crucial step in preparing qubits (atoms) for execution. To optimize this assembly process, we propose a novel quadrant-based rearrangement algorithm that employs a divide-and-conquer strategy and also enables the simultaneous movement of multiple atoms, even across different columns and rows. We implement the algorithm on Field Programmable Gate Arrays (FPGAs) to handle each quadrant independently (hardware-level optimization) while maximizing parallelization. To the best of our knowledge, this is the first hardware acceleration work for atom rearrangement, and it significantly reduces the processing time. This achievement also contributes to the ongoing efforts of tightly integrating quantum accelerators into High-Performance Computing (HPC) systems. Tested on a Zynq RFSoC FPGA at 250 MHz, our hardware implementation is able to complete the rearrangement process of a 30 × 30 compact target array, derived from a 50 × 50 initial loaded array, in approximately 1.0 μs. Compared to a comparable CPU implementation and to state-of-the-art FPGA work, we achieved about 54 x and 300 x speedups in the rearrangement analysis time, respectively. Additionally, the FPGA-based acceleration demonstrates good scalability, allowing for seamless adaptation to varying sizes of the atom array, which makes this algorithm a promising solution for large-scale quantum systems.
Assembling increasingly larger-scale defect-free optical-tweezer-trapped atom arrays is essential for quantum computation and quantum simulations based on atoms. Here, we propose an AI-enabled, rapid, constant-time-overhead rearrangement protocol, and we experimentally assemble defect-free 2D and 3D atom arrays with up to 2024 atoms with a constant-time cost of 60 ms. The AI model calculates the holograms for real-time atom rearrangement. With precise controls over both position and phase, a high-speed spatial light modulator moves all the atoms simultaneously. This protocol can be readily used to generate defect-free arrays of tens of thousands of atoms with current technologies and become a useful toolbox for quantum error correction.
Defect-free atom arrays have emerged as a powerful and versatile platform for quantum sciences and technologies, offering high programmability and promising scalability. The arrays can be prepared by rearranging atoms from a partially loaded initial array to the designated target sites. However, achieving large defect-free arrays presents challenges due to atom loss during rearrangement and the vacuum-limited lifetime which is inversely proportional to the array size. Efficient rearrangement algorithms which minimize time cost and atom loss are crucial for successful atom rearrangement. Here we propose a novel parallel compression algorithm which leverages multiple mobile tweezers to transfer atoms simultaneously. The total time cost could be reduced to scale linearly with the number of target sites. This algorithm can be readily implemented in current experimental setups.
This paper proposes a scalable planning algorithm for creating defect-free atom arrays in neutral-atom systems. The algorithm generates a O(N)\mathcal{O}(\sqrt N)O(N​) time plan for NNN atoms by parallelizing atom transport using a two-dimensional lattice pattern generated by acousto-optic deflectors. Our approach is based on a divide-and-conquer strategy that decomposes an arbitrary reconfiguration problem into at most three one-dimensional shuttling tasks, enabling each atom to be transported with a total transportation cost of O(N)\mathcal{O}(\sqrt N)O(N​). Using the Gale--Ryser theorem, the proposed algorithm provides a highly reliable solution for arbitrary target geometries. We further introduce a peephole optimization technique that improves reconfiguration efficiency for grid target geometries. Numerical simulations on a 632$\times$632 atom array demonstrate that the proposed algorithm achieves a grid configuration plan that reduces the total transportation cost to 1/7 of state-of-the-art algorithms, while resulting in 32%--35% more atom captures. We believe that our scalability improvement contributes to realizing large-scale quantum computers based on neutral atoms. Our experimental code is available from https://github.com/kotamanegi/sqrt-time-atom-reconfigure.
Precisely controllable, defect-free neutral atom arrays have emerged as an outstanding platform for exploring complex quantum many-body systems and advancing quantum simulation and quantum computing. A method that boosts the rapid and efficient rearrangement is of great significance for expanding the scale of atomic arrays. Here, we present a fast parallel atomic array assembly method based on a PPM algorithm, i.e., position matching, path planning, and move in parallel. By optimizing atom-to-target trajectories using reduced position matching and introducing trajectory correction and target exchange in path planning, the PPM method can effectively minimize movement steps and displacement. The simulation of assembling atoms in parallel into arbitrary geometries shows a reduction of 49% and 35% in steps and displacement compared with the previous parallel algorithm, respectively. Our PPM simulation demonstrates the achievement of a defect-free arrangement of 1,024 atoms in only 35 steps.
Dual-species single-atom array in optical tweezers has several advantages over the single-species atom array as a platform for quantum computing and quantum simulation. Thus, creating the defect-free dual-species single-atom array with atom numbers over hundreds is essential. As recent experiments demonstrated, one of the main difficulties lies in designing an efficient algorithm to rearrange the stochastically loaded dual-species atoms arrays into arbitrary demanded configurations. We propose a heuristic connectivity optimization algorithm to provide the near-fewest number of atom moves. Our algorithm introduces the concept of using articulation points in an undirected graph to optimize connectivity as a critical consideration for arranging the atom moving paths. Tested in array size of hundreds atoms and various configurations, our algorithm shows a high success rate (>97%), low extra atom moves ratio, good scalability, and flexibility. Furthermore, we propose a complementary step to solve the problem of atom loss during the rearrangement.