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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2511.22708 |
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| _version_ | 1866917110780788736 |
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| author | Sergeev, Mikhail Paradezhenko, Georgii Rabinovich, Daniil Palyulin, Vladimir V. |
| author_facet | Sergeev, Mikhail Paradezhenko, Georgii Rabinovich, Daniil Palyulin, Vladimir V. |
| contents | Quantum architecture search (QAS) automates the design of parameterized quantum circuits for variational quantum algorithms. The framework finds a well-suited problem-specific structure of a variational ansatz. Among possible implementations of QAS the reinforcement learning (RL) stands out as one of the most promising. Current RL approaches are single-agent-based and show poor scalability with a number of qubits due to the increase of the action space dimension and the computational cost. We propose a novel multi-agent RL algorithm for QAS with each agent acting separately on its own block of a quantum circuit. This procedure allows to significantly accelerate the convergence of the RL-based QAS and reduce its computational cost. We benchmark the proposed algorithm on MaxCut problem on 3-regular graphs and on ground energy estimation for the Schwinger Hamiltonian. In addition, the proposed multi-agent approach naturally fits into the set-up of distributed quantum computing, favoring its implementation on modern intermediate scale quantum devices. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_22708 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Distributed quantum architecture search using multi-agent reinforcement learning Sergeev, Mikhail Paradezhenko, Georgii Rabinovich, Daniil Palyulin, Vladimir V. Quantum Physics Quantum architecture search (QAS) automates the design of parameterized quantum circuits for variational quantum algorithms. The framework finds a well-suited problem-specific structure of a variational ansatz. Among possible implementations of QAS the reinforcement learning (RL) stands out as one of the most promising. Current RL approaches are single-agent-based and show poor scalability with a number of qubits due to the increase of the action space dimension and the computational cost. We propose a novel multi-agent RL algorithm for QAS with each agent acting separately on its own block of a quantum circuit. This procedure allows to significantly accelerate the convergence of the RL-based QAS and reduce its computational cost. We benchmark the proposed algorithm on MaxCut problem on 3-regular graphs and on ground energy estimation for the Schwinger Hamiltonian. In addition, the proposed multi-agent approach naturally fits into the set-up of distributed quantum computing, favoring its implementation on modern intermediate scale quantum devices. |
| title | Distributed quantum architecture search using multi-agent reinforcement learning |
| topic | Quantum Physics |
| url | https://arxiv.org/abs/2511.22708 |