Multiqubit state learning with entangling quantum generative adversarial networks

TL;DR

This paper explores the use of entangling quantum GANs (EQ-GANs) for multiqubit state learning, demonstrating their efficiency in learning quantum circuits, eigenstates, and random states, with potential applications in quantum state loading.

Contribution

The study introduces EQ-GANs for multiqubit learning, showing their advantages over traditional methods and their ability to learn complex quantum states efficiently.

Findings

EQ-GAN learns circuits more efficiently than SWAP test.

EQ-GAN generates high overlap matrix elements for VQE-approximated states.

Capable of learning random states up to six qubits.

Abstract

The increasing success of classical generative adversarial networks (GANs) has inspired several quantum versions of GANs. Fully quantum mechanical applications of such quantum GANs have been limited to one- and two-qubit systems. In this paper, we investigate the entangling quantum GAN (EQ-GAN) for multiqubit learning. We show that the EQ-GAN can learn a circuit more efficiently compared with a SWAP test. We also consider the EQ-GAN for learning eigenstates that are variational quantum eigensolver (VQE)-approximated, and find that it generates excellent overlap matrix elements when learning VQE states of small molecules. However, this does not directly translate into a good estimate of the energy due to a lack of phase estimation. Finally, we consider random state learning with the EQ-GAN for up to six qubits, using different two-qubit gates, and show that it is capable of learning completely random quantum states, something which could be useful in quantum state loading.