Atlas Enables Defect-Free Neutral-Atom Quantum Arrays with Efficient Rearrangement under Probabilistic Atom Loss

Neutral-atom quantum computing relies on precisely arranged atoms to encode and process information, but creating perfect, defect-free arrays remains a significant challenge, as random loading and atom loss frequently disrupt assembly. Otto Savola and Alexandru Paler, both from Aalto University, present a new algorithm, ATLAS, that efficiently rearranges atoms to create these defect-free subarrays, even when accounting for realistic limitations such as acceleration limits, transfer times, and the probability of losing atoms during movement. The team demonstrates that ATLAS achieves remarkably high fill rates, exceeding 90% within just a few iterations, and retains over 90% of atoms even with substantial loss rates, representing a substantial improvement in both robustness and scalability compared to existing methods. This achievement paves the way for building larger, more reliable neutral-atom quantum computers by offering a practical solution to the critical problem of defect-free atom array assembly.

Efficient Atom Rearrangement for Scalable Quantum Arrays

This work introduces ATLAS, a novel algorithm designed to efficiently rearrange neutral atoms into defect-free arrays, crucial for building scalable quantum computers. The algorithm addresses the challenge of moving individual atoms without introducing errors, a problem that limits the size and reliability of quantum processors. ATLAS achieves this through a combination of strategies, beginning with a planning phase that determines the optimal rearrangement strategy before execution. It leverages the ability to move multiple atoms simultaneously, significantly speeding up the process, and importantly, estimates the necessary initial array size to account for the probability of atoms being lost during movement.

This loss-aware approach ensures a high probability of achieving a defect-free final array, and further optimization through increased parallelism reduces the number of required moves. The results demonstrate that ATLAS achieves sublinear move scaling, meaning the time required to rearrange the array grows slower than linearly, and requires a linearly increasing initial array size, making it highly scalable. ATLAS maintains high fill rates and remains robust even with realistic atom loss, outperforming existing methods in both scalability and atom utilization. In essence, ATLAS is a sophisticated algorithm that optimizes the creation of high-quality neutral atom arrays, paving the way for more powerful and scalable quantum computers.

Atom Rearrangement Algorithm for Defect-Free Arrays

Scientists developed ATLAS, an open-source algorithm designed to convert randomly loaded optical lattices into defect-free subarrays, addressing a critical challenge in neutral-atom quantum computing. The methodology efficiently rearranges atoms while accounting for realistic physical limitations, including finite acceleration, transfer time, and the probability of atom loss during movement. The process begins with planning, where the algorithm computes optimal batches of parallel moves on a virtual, lossless array, effectively mapping an ideal rearrangement strategy. During execution, these pre-planned moves are implemented, but crucially, they are adapted to account for probabilistic atom loss, maximizing the expected number of atoms retained throughout the rearrangement.

Monte Carlo simulations across various lattice sizes, loading probabilities, and loss rates demonstrate that the algorithm consistently achieves fill rates exceeding 99% within just six iterations, while maintaining over 90% atom retention even at low loss rates. Furthermore, the algorithm exhibits sublinear move scaling and linear growth of required initial size, significantly outperforming prior methods in both robustness and scalability. This innovative approach offers a practical pathway toward building larger, more reliable neutral-atom quantum arrays, essential for advancing quantum computation.

Defect-Free Atom Arrays via Optimal Transport

This work presents ATLAS, an innovative algorithm designed to create defect-free subarrays within neutral-atom quantum computing systems. Researchers achieved high fill rates, consistently exceeding 99% within six iterations, even with realistic atom loss probabilities incorporated into the simulations. Experiments demonstrate that ATLAS efficiently converts randomly loaded lattices into highly ordered configurations, a crucial step towards building scalable and reliable quantum processors. The algorithm operates by planning optimal, parallel atom movements on a virtual, lossless array before executing them on a physical lattice, accounting for finite acceleration, transfer time, and the probability of atom loss during each move.

Monte Carlo simulations across lattice sizes ranging from 10×10 to 100×100, with varying loading probabilities and loss rates, consistently yielded high performance. Specifically, with a 50×50 lattice and an occupation probability of 0. 7, a perfect fill rate was consistently achieved after just one iteration in the absence of atom loss. Even with a moderate loss probability, the algorithm converged rapidly, maintaining a perfect fill rate. Increasing the loss probability increased the number of iterations required, but the fill rate remained perfect, demonstrating the algorithm’s robustness. Retention rates were also measured, revealing that the algorithm is most efficient with larger lattices, with the retention rate plateauing as lattice size increases. The computational time scales approximately cubically with initial lattice width, but the move count scales sublinearly with the number of sites, indicating excellent scalability and suggesting that ATLAS can efficiently manage increasingly large neutral-atom arrays, paving the way for more powerful and complex quantum computations.

Defect-Free Atom Arrays via Scalable Rearrangement

The team presents ATLAS, an efficient algorithm for rearranging atoms in neutral-atom quantum computing systems, achieving defect-free arrays despite realistic limitations such as atom loss during movement. By separating the planning and execution phases, and by estimating the necessary initial array size to account for potential losses, ATLAS reliably produces high fill rates and retains a significant number of atoms across a range of lattice sizes and loss conditions. Monte Carlo simulations demonstrate that the number of moves required scales sublinearly with lattice size, and the initial size needed grows linearly, representing an improvement over existing methods in both scalability and atom utilization. The algorithm’s performance is further enhanced through extensive parallelization, reducing the move-scaling exponent to a level comparable to the best multi-tweezer techniques while maintaining higher atom retention rates. While effective across a broad range of conditions, the authors acknowledge that ATLAS performs less optimally with smaller lattices, as it is designed to maximize atom placement in larger arrays. Future work may focus on adapting the algorithm for improved performance in these smaller configurations, potentially broadening its applicability across diverse quantum computing architectures.