论文标题
与纠缠量子生成对抗网络的多Qubit状态学习
Multiqubit state learning with entangling quantum generative adversarial networks
论文作者
论文摘要
经典生成对抗网络（GAN）的成功越来越多，启发了几种量子版本的gan。此类量子剂的完全量子机械应用仅限于单位和两分的系统。在本文中，我们研究了用于多等级学习的纠缠量子gan（EQ-GAN）。我们表明，与交换测试相比，EQ-GAN可以更有效地学习电路。我们还考虑了学习特征状态的EQ-GAN，这些特征状态是变异量子本特征（VQE）及时的，并发现在学习小分子的VQE状态时，它会产生出色的重叠矩阵元素。但是，由于缺乏相位估计，这并不能直接转化为对能量的良好估计。最后，我们使用EQ-GAN使用不同的六个Qubits，使用不同的两倍的门来考虑随机的状态学习，并表明它能够学习完全随机的量子状态，这在量子状态加载中可能有用。
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.
