Qubit-Wise Architecture Search Method for Variational Quantum Circuits

arXiv:2403.04268v1 Announce Type: cross
Abstract: Considering the noise level limit, one crucial aspect for quantum machine learning is to design a high-performing variational quantum circuit architecture with small number of quantum gates. As the classical neural architecture search (NAS), quantum architecture search methods (QAS) employ methods like reinforcement learning, evolutionary algorithms and supernet optimiza-tion to improve the search efficiency. In this paper, we propose a novel qubit-wise architec-ture search (QWAS) method, which progres-sively search one-qubit configuration per stage, and combine …

abstract algorithms architecture arxiv cs.lg design evolutionary algorithms gates machine machine learning nas neural architecture search noise quant-ph quantum quantum gates qubit reinforcement reinforcement learning search small type wise