Quantum circuits as a game: A reinforcement learning agent for quantum compilation and its application to reconfigurable neutral atom arrays

Nakaji, Kouhei, Wurtz, Jonathan, Huang, Haozhe, Calderón, Luis Mantilla, et al. · 2025

| 编号 NA132 | 主题 强化学习与智能体 |

摘要

摘要保留原文（英文）

摘要取自 arXiv / 出版方元数据，未作翻译，以保证与原文一致。

2025 · 预印本；未核实后续正式发表 We introduce the “quantum circuit daemon” (QC-Daemon), a reinforcement learning agent for compiling quantum device operations aimed at efficient quantum hardware execution. We apply QC-Daemon to the move synthesis problem called the Atom Game, which involves orchestrating parallel circuits on reconfigurable neutral atom arrays. In our numerical simulation, the QC-Daemon is implemented by two different types of transformers with a physically motivated architecture and trained by a reinforcement learning algorithm. We observe a reduction of the logarithmic infidelity for various benchmark problems up to 100 qubits by intelligently changing the layout of atoms. Additionally, we demonstrate the transferability of our approach: a Transformer-based QC-Daemon trained on a diverse set of circuits successfully generalizes its learned strategy to previously unseen circuits.

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