量子生成对抗学习在量子图像处理中的应用

Wanghao Ren, Zhiming Li, Hailing Li, Yang Li, Chunwei Zhang, Xiaoqian Fu
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引用次数: 0

摘要

量子机器学习是近年来量子学界的一个热门话题。量子机器学习和图像处理的结合也是研究人员关注的问题之一。随着深度学习对图像处理的推广,图像处理领域也显示出惊人的潜力。量子计算与人工智能的结合,不仅可以发挥量子计算的计算能力,还可以在图像处理领域找到更多的应用。本文将量子生成对抗学习与图像处理领域相结合。设计了一个量子生成对抗网络来加载和学习经典图像数据。数值模拟直观地表明,量子算法也可以有效地处理图像。在我们的方案中,使用N个量子比特可以加载2N个经典比特的图像数据。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Application of Quantum Generative Adversarial Learning in Quantum Image Processing
Quantum machine learning is a hot topic in the quantum community recently. The combination of quantum machine learning and image processing is also one of the researchers’ concerns. With the promotion of deep learning to process images, the field of image processing has also shown amazing potential. The combination of quantum computing and artificial intelligence can not only exert the computing power of quantum computing but also find more applications in the field of image processing. This article combines quantum generative adversarial learning with the field of image processing. A quantum generative adversarial network is designed to load and learn classical image data. Numerical simulations intuitively show that quantum algorithms can also effectively process images. In our scheme, using N qubits can load 2N classical bits of image data.
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