An RNN-policy gradient approach for quantum architecture search

TL;DR

This paper introduces a deep reinforcement learning method for automatically designing quantum circuit architectures, improving performance and efficiency in quantum data classification tasks.

Contribution

It presents a novel RNN-policy gradient approach for quantum architecture search, combining reinforcement learning with layer-based sampling for better results.

Findings

Achieves higher classification accuracy with optimized quantum circuits.

Produces circuits with fewer gates and parameters.

Demonstrates improved efficiency over manual design methods.

Abstract

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.