  Generative adversarial networks (GANs) are one of the most widely adopted
semisupervised and unsupervised machine learning methods for high-definition
image, video, and audio generation. In this work, we propose a new type of
architecture for quantum generative adversarial networks (entangling quantum
GAN, EQ-GAN) that overcomes some limitations of previously proposed quantum
GANs. Leveraging the entangling power of quantum circuits, EQ-GAN guarantees
the convergence to a Nash equilibrium under minimax optimization of the
discriminator and generator circuits by performing entangling operations
between both the generator output and true quantum data. We show that EQ-GAN
has additional robustness against coherent errors and demonstrate the
effectiveness of EQ-GAN experimentally in a Google Sycamore superconducting
quantum processor. By adversarially learning efficient representations of
quantum states, we prepare an approximate quantum random access memory (QRAM)
and demonstrate its use in applications including the training of quantum
neural networks.
