Unitary Synthesis of Clifford+T Circuits with Reinforcement Learning
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arXiv
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| Format: | Preprint |
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2024
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| author | Rietsch, Sebastian Dubey, Abhishek Y. Ufrecht, Christian Periyasamy, Maniraman Plinge, Axel Mutschler, Christopher Scherer, Daniel D. |
| author_facet | Rietsch, Sebastian Dubey, Abhishek Y. Ufrecht, Christian Periyasamy, Maniraman Plinge, Axel Mutschler, Christopher Scherer, Daniel D. |
| contents | This paper presents a deep reinforcement learning approach for synthesizing unitaries into quantum circuits. Unitary synthesis aims to identify a quantum circuit that represents a given unitary while minimizing circuit depth, total gate count, a specific gate count, or a combination of these factors. While past research has focused predominantly on continuous gate sets, synthesizing unitaries from the parameter-free Clifford+T gate set remains a challenge. Although the time complexity of this task will inevitably remain exponential in the number of qubits for general unitaries, reducing the runtime for simple problem instances still poses a significant challenge. In this study, we apply the tree-search method Gumbel AlphaZero to solve the problem for a subset of exactly synthesizable Clifford+T unitaries. Our method effectively synthesizes circuits for up to five qubits generated from randomized circuits with up to 60 gates, outperforming existing tools like QuantumCircuitOpt and MIN-T-SYNTH in terms of synthesis time for larger qubit counts. Furthermore, it surpasses Synthetiq in successfully synthesizing random, exactly synthesizable unitaries. These results establish a strong baseline for future unitary synthesis algorithms. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_14865 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Unitary Synthesis of Clifford+T Circuits with Reinforcement Learning Rietsch, Sebastian Dubey, Abhishek Y. Ufrecht, Christian Periyasamy, Maniraman Plinge, Axel Mutschler, Christopher Scherer, Daniel D. Quantum Physics This paper presents a deep reinforcement learning approach for synthesizing unitaries into quantum circuits. Unitary synthesis aims to identify a quantum circuit that represents a given unitary while minimizing circuit depth, total gate count, a specific gate count, or a combination of these factors. While past research has focused predominantly on continuous gate sets, synthesizing unitaries from the parameter-free Clifford+T gate set remains a challenge. Although the time complexity of this task will inevitably remain exponential in the number of qubits for general unitaries, reducing the runtime for simple problem instances still poses a significant challenge. In this study, we apply the tree-search method Gumbel AlphaZero to solve the problem for a subset of exactly synthesizable Clifford+T unitaries. Our method effectively synthesizes circuits for up to five qubits generated from randomized circuits with up to 60 gates, outperforming existing tools like QuantumCircuitOpt and MIN-T-SYNTH in terms of synthesis time for larger qubit counts. Furthermore, it surpasses Synthetiq in successfully synthesizing random, exactly synthesizable unitaries. These results establish a strong baseline for future unitary synthesis algorithms. |
| title | Unitary Synthesis of Clifford+T Circuits with Reinforcement Learning |
| topic | Quantum Physics |
| url | https://arxiv.org/abs/2404.14865 |