2024-05-13
An RNN–policy gradient approach for quantum architecture search
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.
