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Experimental Quantum Generative Adversarial Networks for Image Generation | | He-Liang Huang
; Yuxuan Du
; Ming Gong
; Youwei Zhao
; Yulin Wu
; Chaoyue Wang
; Shaowei Li
; Futian Liang
; Jin Lin
; Yu Xu
; Rui Yang
; Tongliang Liu
; Min-Hsiu Hsieh
; Hui Deng
; Hao Rong
; Cheng-Zhi Peng
; Chao-Yang Lu
; Yu-Ao Chen
; Dacheng Tao
; Xiaobo Zhu
; Jian-Wei Pan
; | | Date: |
13 Oct 2020 | | Abstract: | Quantum machine learning is expected to be one of the first practical
applications of near-term quantum devices. Pioneer theoretical works suggest
that quantum generative adversarial networks (GANs) may exhibit a potential
exponential advantage over classical GANs, thus attracting widespread
attention. However, it remains elusive whether quantum GANs implemented on
near-term quantum devices can actually solve real-world learning tasks. Here,
we devise a flexible quantum GAN scheme to narrow this knowledge gap, which
could accomplish image generation with arbitrarily high-dimensional features,
and could also take advantage of quantum superposition to train multiple
examples in parallel. For the first time, we experimentally achieve the
learning and generation of real-world hand-written digit images on a
superconducting quantum processor. Moreover, we utilize a gray-scale bar
dataset to exhibit the competitive performance between quantum GANs and the
classical GANs based on multilayer perceptron and convolutional neural network
architectures, respectively, benchmarked by the Fr’echet Distance score. Our
work provides guidance for developing advanced quantum generative models on
near-term quantum devices and opens up an avenue for exploring quantum
advantages in various GAN-related learning tasks. | | Source: | arXiv, 2010.06201 | | Services: | Forum | Review | PDF | Favorites | |
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