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arXiv · 2204.09689

Multiqubit state learning with entangling quantum generative adversarial networks

S. E. Rasmussen·N. T. Zinner
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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.

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BibTeXRIS

S. E. Rasmussen, N. T. Zinner. 2022-04-20. Multiqubit state learning with entangling quantum generative adversarial networks. https://doi.org/10.1103/physreva.106.032429

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