期刊
PHYSICAL REVIEW LETTERS
卷 124, 期 13, 页码 -出版社
AMER PHYSICAL SOC
DOI: 10.1103/PhysRevLett.124.130502
关键词
-
资金
- Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) [A02, A06]
Entangled states are an important resource for quantum computation, communication, metrology, and the simulation of many-body systems. However, noise limits the experimental preparation of such states. Classical data can be efficiently denoised by autoencoders-neural networks trained in unsupervised manner. We develop a novel quantum autoencoder that successfully denoises Greenberger-Horne-Zeilinger, W, Dicke, and cluster states subject to spin-flip errors and random unitary noise. Various emergent quantum technologies could benefit from the proposed unsupervised quantum neural networks.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据