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文章基本信息

  • 标题:Supervised Quantum Learning without Measurements
  • 本地全文:下载
  • 作者:Unai Alvarez-Rodriguez ; Lucas Lamata ; Pablo Escandell-Montero
  • 期刊名称:Scientific Reports
  • 电子版ISSN:2045-2322
  • 出版年度:2017
  • 卷号:7
  • 期号:1
  • DOI:10.1038/s41598-017-13378-0
  • 语种:English
  • 出版社:Springer Nature
  • 摘要:We propose a quantum machine learning algorithm for efficiently solving a class of problems encoded in quantum controlled unitary operations. The central physical mechanism of the protocol is the iteration of a quantum time-delayed equation that introduces feedback in the dynamics and eliminates the necessity of intermediate measurements. The performance of the quantum algorithm is analyzed by comparing the results obtained in numerical simulations with the outcome of classical machine learning methods for the same problem. The use of time-delayed equations enhances the toolbox of the field of quantum machine learning, which may enable unprecedented applications in quantum technologies.
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