Distributed quantum architecture search using multi-agent reinforcement learning

TL;DR

This paper introduces a multi-agent reinforcement learning approach for quantum architecture search that improves scalability and efficiency, enabling distributed quantum computing applications.

Contribution

A novel multi-agent RL algorithm for quantum architecture search that enhances scalability and reduces computational costs compared to single-agent methods.

Findings

Accelerates convergence of quantum architecture search.

Reduces computational costs significantly.

Effective on MaxCut and Hamiltonian energy estimation tasks.

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.