Quantum Wasserstein Generative Adversarial Networks

NeurIPS 2019 Shouvanik ChakrabartiYiming HuangTongyang LiSoheil FeiziXiaodi Wu

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... (read more)

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