Distributed quantum architecture search using multi-agent reinforcement learning

• URL: http://arxiv.org/abs/2511.22708v1
• Date: Thu, 27 Nov 2025 19:00:00 GMT
• Title: Distributed quantum architecture search using multi-agent reinforcement learning
• Authors: Mikhail Sergeev, Georgii Paradezhenko, Daniil Rabinovich, Vladimir V. Palyulin,
• Abstract summary: Quantum architecture search (QAS) automates the design of parameterized quantum circuits for variational quantum algorithms.<n>We propose a novel multi-agent RL algorithm for QAS with each agent acting separately on its own block of a quantum circuit.<n>We benchmark the proposed algorithm on MaxCut problem on 3-regular graphs and on ground energy estimation for the Schwinger Hamiltonian.
• Score: 0.09999629695552194
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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