  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 with Monte Carlo Tree
Search al-gorithm to find good quantum architectures by partitioning the search
space into several good and bad subregions. The numerical experimental results
indicate that our proposed method can balance the exploration and exploitation
of cir-cuit performance and size in some real-world tasks, such as MNIST,
Fashion and MOSI. As far as we know, QWAS achieves the state-of-art re-sults of
all tasks in the terms of accuracy and circuit size.
