Quantum Wasserstein Generative Adversarial Networks
Shouvanik Chakrabarti · Huang Yiming · Tongyang Li · Soheil Feizi · Xiaodi Wu

Wed Dec 11th 10:45 AM -- 12:45 PM @ East Exhibition Hall B + C #119

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.

Author Information

Shouvanik Chakrabarti (University of Maryland)
Huang Yiming (University of Electronic Science and Technology of China; University of Maryland)
Tongyang Li (University of Maryland)

Tongyang Li is a Ph.D. candidate at the Department of Computer Science, University of Maryland. He received B.E. from Institute for Interdisciplinary Information Sciences, Tsinghua University and B.S. from Department of Mathematical Sciences, Tsinghua University, both in 2015; he also received a Master degree from Department of Computer Science, University of Maryland in 2018. He is a recipient of the IBM Ph.D. Fellowship and the NSF QISE-NET Triplet Award, and was a recipient of the Lanczos Fellowship during 2015-2017.

Soheil Feizi (University of Maryland)
Xiaodi Wu (University of Maryland)

More from the Same Authors