Quantum circuits as a game: A reinforcement learning agent for quantum compilation and its application to reconfigurable neutral atom arrays
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arXiv
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866908395471110144 |
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| author | Nakaji, Kouhei Wurtz, Jonathan Huang, Haozhe Calderón, Luis Mantilla Panicker, Karthik Kyoseva, Elica Aspuru-Guzik, Alán |
| author_facet | Nakaji, Kouhei Wurtz, Jonathan Huang, Haozhe Calderón, Luis Mantilla Panicker, Karthik Kyoseva, Elica Aspuru-Guzik, Alán |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_05536 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | 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 Panicker, Karthik Kyoseva, Elica Aspuru-Guzik, Alán Quantum Physics 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. |
| title | Quantum circuits as a game: A reinforcement learning agent for quantum compilation and its application to reconfigurable neutral atom arrays |
| topic | Quantum Physics |
| url | https://arxiv.org/abs/2506.05536 |