# Quantum Architecture Search via Deep Reinforcement Learning

@article{Kuo2021QuantumAS, title={Quantum Architecture Search via Deep Reinforcement Learning}, author={En-Jui Kuo and Yao-Lung L. Fang and Samuel Yen-Chi Chen}, journal={ArXiv}, year={2021}, volume={abs/2104.07715} }

Abstract Recent advances in quantum computing have drawn considerable attention to building realistic application for and using quantum computers. However, designing a suitable quantum circuit architecture requires expert knowledge. For example, it is non-trivial to design a quantum gate sequence for generating a particular quantum state with as fewer gates as possible. We propose a quantum architecture search framework with the power of deep reinforcement learning (DRL) to address this… Expand

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