



01810nas a2200169   4500008004100000245005600041210005600097260001500153490000700168520133300175100002701508700001801535700001701553700001801570700001501588856003701603       2019                        eng d00aQuantum Wasserstein Generative Adversarial Networks0 aQuantum Wasserstein Generative Adversarial Networks  c2019/10/310 v323 a<p>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.</p>1 aChakrabarti, Shouvanik1 aHuang, Yiming1 aLi, Tongyang1 aFeizi, Soheil1 aWu, Xiaodi  uhttps://arxiv.org/abs/1911.00111