Search Smarter, Not Harder: A Scalable, High-Quality Zoned Neutral Atom Compiler

TL;DR

This paper introduces a scalable, goal-directed compilation algorithm for large-scale zoned neutral atom quantum architectures, significantly reducing memory usage and atom rearrangement overhead.

Contribution

It presents Iterative Diving Search (IDS), a novel scalable compilation method that overcomes memory limitations of prior approaches and improves rearrangement efficiency.

Findings

Successfully compiles circuits with thousands of qubits.

Reduces atom rearrangement overhead by 28.1%.

Demonstrates scalability and efficiency of the proposed method.

Abstract

Zoned neutral atom architectures are emerging as a promising platform for large-scale quantum computing. Their growing scale, however, creates a critical need for efficient and automated compilation solutions. Yet, existing methods fail to scale to the thousands of qubits these devices promise. State-of-the-art compilers, in particular, suffer from immense memory requirements that limit them to small-scale problems. This work proposes a scalable compilation strategy that "searches smarter, not harder". We introduce Iterative Diving Search (IDS), a goal-directed search algorithm that avoids the memory issues of previous methods, and relaxed routing, an optimization to mitigate atom rearrangement overhead. Our evaluation confirms that this approach compiles circuits with thousands of qubits and, in addition, even reduces rearrangement overhead by 28.1% on average. The complete code is publicly available in open-source as part of the Munich Quantum Toolkit (MQT) at https://github.com/munich-quantum-toolkit/qmap.