Quantum Wasserstein Generative Adversarial Networks-猫眼课题宝

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发表时间：

2019-10

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ArXiv

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其他

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作者：

Shouvanik Chakrabarti;Yiming Huang;Tongyang Li;S. Feizi;Xiaodi Wu

量子生成模型的研究动机良好，不仅因为它在量子机器学习和量子化学中的重要性，还因为它在近期量子机器上的实现前景。受到之前经典和量子生成模型对抗训练研究的启发，我们提出了量子 Wasserstein 生成对抗网络（WGAN）的第一个设计，该网络已被证明可以提高量子生成模型对抗训练的鲁棒性和可扩展性，即使在有噪声的量子硬件上也是如此。具体来说，我们提出了量子数据之间 Wasserstein 半度量的定义，它继承了其经典对应物的一些关键理论优点。我们还演示了如何将量子 Wasserstein 半度量转化为可以在量子机器上有效实现的量子 WGAN 的具体设计。我们的数值研究通过量子系统的经典模拟表明，我们的量子 WGAN 比其他量子 GAN 方案具有更稳健和可扩展的数值性能。作为一个令人惊讶的应用，我们的量子 WGAN 已用于生成约 50 个门的 3 量子位量子电路，该电路很好地近似了使用标准技术需要超过 10k 个门的 3 量子位一维哈密顿模拟电路。

The study of quantum generative models is well-motivated, not only because of its importance in quantum machine learning and quantum chemistry but also because of the perspective of its implementation on near-term quantum machines. Inspired by previous studies on the adversarial training of classical and quantum generative models, we propose the first design of quantum Wasserstein Generative Adversarial Networks (WGANs), which has been shown to improve the robustness and the scalability of the adversarial training of quantum generative models even on noisy quantum hardware. Specifically, we propose a definition of the Wasserstein semimetric between quantum data, which inherits a few key theoretical merits of its classical counterpart. We also demonstrate how to turn the quantum Wasserstein semimetric into a concrete design of quantum WGANs that can be efficiently implemented on quantum machines. Our numerical study, via classical simulation of quantum systems, shows the more robust and scalable numerical performance of our quantum WGANs over other quantum GAN proposals. As a surprising application, our quantum WGAN has been used to generate a 3-qubit quantum circuit of ~50 gates that well approximates a 3-qubit 1-d Hamiltonian simulation circuit that requires over 10k gates using standard techniques.

Quantum Wasserstein Generative Adversarial Networks

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