RH: An Architecture for Redesigning Quantum Circuits on Quantum Hardware Devices

In this paper we present an architecture that enables the redesign of large-scale quantum circuits on quantum hardware based on the entangling quantum generative adversarial network (EQ-GAN). Specifically, by prepending a random quantum circuit module to the standard EQ-GAN framework, we extend its capability from quantum state learning to unitary transformation learning. The completeness of this architecture is theoretically proved. Moreover, an efficient local random circuit is proposed, which significantly enhances the practicality of our architecture. For concreteness, we apply this architecture to three crucial applications in circuit optimization, including the equivalence checking of (non-) parameterized circuits, as well as the variational reconstruction of quantum circuits. The feasibility of our approach is demonstrated by excellent results in both classical and noisy intermediate-scale quantum (NISQ) hardware implementations. We believe our work will facilitate the implementation and validation of the advantages of quantum algorithms.