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

2025-12-15

AI Summary

Compiler scalability for thousand-qubit neutral-atom quantum computers is hindered by excessive memory consumption and inability to handle large-scale quantum circuits. Method: We propose Iterative Deepening Search (IDS) to replace memory-intensive search algorithms and design a physics-aware relaxed routing strategy that models hardware constraints to minimize atom rearrangement overhead. Our approach is integrated into the MQT open-source framework, enabling hardware-aware qubit mapping and dynamic rearrangement optimization. Contribution/Results: The method successfully compiles quantum circuits with thousands of qubits, reducing average rearrangement operations by 28.1% and significantly lowering memory usage. It represents the first efficient, large-scale quantum circuit compilation solution tailored specifically for neutral-atom platforms. The implementation is publicly available as open-source software.

Technology Category

• Machine Learning: Quantum Machine Learning
• Search and Optimization: Mixed Discrete/Continuous Search
• Planning, Routing, and Scheduling: Planning with Language Models

Application Category

• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets
• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines
• Graph Algorithms and Modeling for the Web: Querying, indexing, and retrieval in Web-related graphs

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.

Problem

• Scalable compilation for large-scale quantum computing
• Reducing memory requirements in quantum circuit compilation
• Optimizing atom rearrangement in zoned neutral atom architectures

Innovation

• Iterative Diving Search algorithm for scalable compilation
• Relaxed routing optimization to reduce rearrangement overhead
• Open-source integration within Munich Quantum Toolkit (MQT)