Practical and efficient quantum circuit synthesis and transpiling with Reinforcement Learning
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866912247976034304 |
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| author | Kremer, David Villar, Victor Paik, Hanhee Duran, Ivan Faro, Ismael Cruz-Benito, Juan |
| author_facet | Kremer, David Villar, Victor Paik, Hanhee Duran, Ivan Faro, Ismael Cruz-Benito, Juan |
| contents | This paper demonstrates the integration of Reinforcement Learning (RL) into quantum transpiling workflows, significantly enhancing the synthesis and routing of quantum circuits. By employing RL, we achieve near-optimal synthesis of Linear Function, Clifford, and Permutation circuits, up to 9, 11 and 65 qubits respectively, while being compatible with native device instruction sets and connectivity constraints, and orders of magnitude faster than optimization methods such as SAT solvers. We also achieve significant reductions in two-qubit gate depth and count for circuit routing up to 133 qubits with respect to other routing heuristics such as SABRE. We find the method to be efficient enough to be useful in practice in typical quantum transpiling pipelines. Our results set the stage for further AI-powered enhancements of quantum computing workflows. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_13196 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Practical and efficient quantum circuit synthesis and transpiling with Reinforcement Learning Kremer, David Villar, Victor Paik, Hanhee Duran, Ivan Faro, Ismael Cruz-Benito, Juan Quantum Physics Artificial Intelligence This paper demonstrates the integration of Reinforcement Learning (RL) into quantum transpiling workflows, significantly enhancing the synthesis and routing of quantum circuits. By employing RL, we achieve near-optimal synthesis of Linear Function, Clifford, and Permutation circuits, up to 9, 11 and 65 qubits respectively, while being compatible with native device instruction sets and connectivity constraints, and orders of magnitude faster than optimization methods such as SAT solvers. We also achieve significant reductions in two-qubit gate depth and count for circuit routing up to 133 qubits with respect to other routing heuristics such as SABRE. We find the method to be efficient enough to be useful in practice in typical quantum transpiling pipelines. Our results set the stage for further AI-powered enhancements of quantum computing workflows. |
| title | Practical and efficient quantum circuit synthesis and transpiling with Reinforcement Learning |
| topic | Quantum Physics Artificial Intelligence |
| url | https://arxiv.org/abs/2405.13196 |