論文

査読有り 本文へのリンクあり
2019年1月2日

Quantum Analog-Digital Conversion

PHYSICAL REVIEW A
  • Kosuke Mitarai
  • ,
  • Masahiro Kitagawa
  • ,
  • Keisuke Fujii

99
1
開始ページ
012301
終了ページ
記述言語
英語
掲載種別
DOI
10.1103/PhysRevA.99.012301
出版者・発行元
AMER PHYSICAL SOC

Many quantum algorithms, such as Harrow-Hassidim-Lloyd (HHL) algorithm,
depend on oracles that efficiently encode classical data into a quantum state.
The encoding of the data can be categorized into two types; analog-encoding
where the data are stored as amplitudes of a state, and digital-encoding where
they are stored as qubit-strings. The former has been utilized to process
classical data in an exponentially large space of a quantum system, where as
the latter is required to perform arithmetics on a quantum computer. Quantum
algorithms like HHL achieve quantum speedups with a sophisticated use of these
two encodings. In this work, we present algorithms that converts these two
encodings to one another. While quantum digital-to-analog conversions have
implicitly been used in existing quantum algorithms, we reformulate it and give
a generalized protocol that works probabilistically. On the other hand, we
propose an deterministic algorithm that performs a quantum analog-to-digital
conversion. These algorithms can be utilized to realize high-level quantum
algorithms such as a nonlinear transformation of amplitude of a quantum state.
As an example, we construct a "quantum amplitude perceptron", a quantum version
of neural network, and hence has a possible application in the area of quantum
machine learning.

リンク情報
DOI
https://doi.org/10.1103/PhysRevA.99.012301
arXiv
http://arxiv.org/abs/arXiv:1805.11250
Web of Science
https://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcAuth=JSTA_CEL&SrcApp=J_Gate_JST&DestLinkType=FullRecord&KeyUT=WOS:000454760900001&DestApp=WOS_CPL
Arxiv Url
http://arxiv.org/abs/1805.11250v2
Arxiv Url
http://arxiv.org/pdf/1805.11250v2 本文へのリンクあり
ID情報
  • DOI : 10.1103/PhysRevA.99.012301
  • ISSN : 2469-9926
  • eISSN : 2469-9934
  • arXiv ID : arXiv:1805.11250
  • Web of Science ID : WOS:000454760900001

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