  Variational quantum circuits are one of the promising ways to exploit the
advantages of quantum computing in the noisy intermediate-scale quantum
technology era. The design of the quantum circuit architecture might greatly
affect the performance capability of the quantum algorithms. The quantum
architecture search is the process of automatically designing quantum circuit
architecture, aiming at finding the optimal quantum circuit composition
architecture by the algorithm for a given task, so that the algorithm can learn
to design the circuit architecture. Compared to manual design, quantum
architecture search algorithms are more effective in finding quantum circuits
with better performance capabilities. In this paper, based on the deep
reinforcement learning, we propose an approach for quantum circuit architecture
search. The sampling of the circuit architecture is learnt through
reinforcement learning based controller. Layer-based search is also used to
accelerate the computational efficiency of the search algorithm. Applying to
data classification tasks we show that the method can search for quantum
circuit architectures with better accuracies. Moreover, the circuit has a
smaller number of quantum gates and parameters.
