Qubit-Wise Architecture Search Method for Variational Quantum Circuits

To develop a strategy to design VQC in an automated way, i.e. quantum architecture search (QAS), some researchers Considering the noise level limit, one crucial aspect have turned their attention to the classical Neural Architecture for quantum machine learning is to design a highperforming Search (NAS) framework. NAS focuses on automating variational quantum circuit architecture the design of neural network structures [Elsken et al., with small number of quantum gates. As the classical 2019], but often grapple with the challenge of evaluating a neural architecture search (NAS), quantum architecture vast number of possible network architectures. The Monte search methods (QAS) employ methods Carlo Tree Search (MCTS) algorithm addresses this issue by like reinforcement learning, evolutionary algorithms iteratively exploring and evaluating segments of the search and supernet optimization to improve the space, thereby identifying promising neural network structures search efficiency. In this paper, we propose a novel without exhaustive enumeration [Silver et al., 2016; qubit-wise architecture search (QWAS) method, Wang et al., 2020]. However, the efficiency of the search is which progressively search one-qubit configuration significantly influenced by the manually predefined action per stage, and combine with Monte Carlo Tree space before the tree construction. To address this issue, Search algorithm to find good quantum architectures [Wang et al., 2021] proposed an improved MCTS-based algorithm by partitioning the search space into several called Latent Action Neural Architecture Search good and bad subregions. The numerical experimental (LaNAS) that learns a latent action space that best fits the results indicate that our proposed method can problem to be solved.