Quantum Reinforcement Learning for Quantum Architecture Search

This paper presents a quantum architecture search (QAS) framework using quantum reinforcement learning (QRL) to generate quantum gate sequences for multi-qubit GHZ states. The proposed framework employs the asynchronous advantage actor-critic (A3C) algorithm to optimize the QRL agent, which has access to Pauli-X, Y, Z expectation values and a predefined set of quantum operations. Our approach does not require any prior knowledge of quantum physics. The framework can be used with other QRL architectures or optimization methods to explore gate synthesis and compilation for various quantum states.