Quantum analog-digital conversion

Mitarai, Kosuke; Kitagawa, Masahiro; Fujii, Keisuke

Physical Review A, 2019, 99(1), 012301

Number of Access:645（2025-09-03 11:38 Counts）

Identifier to cite or link to this item: https://hdl.handle.net/11094/77644

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Title
Quantum analog-digital conversion
Creator
NRID
1000050855111 1000050855111
ORCID
0000-0002-9056-316X 0000-0002-9056-316X
researchmap permalink
kosuke-mitarai kosuke-mitarai
Scopus Author ID
57203861251 57203861251
CreatorName
Mitarai, Kosuke
NRID
1000020252629 1000020252629
ORCID
0000-0002-7434-6472 0000-0002-7434-6472
researchmap permalink
read0079549 read0079549
Scopus Author ID
8326938600 8326938600
CreatorName
Kitagawa, Masahiro
NRID
1000040708640 1000040708640
ORCID
0000-0003-0390-9984 0000-0003-0390-9984
researchmap permalink
7000009401 7000009401
Scopus Author ID
57204923718 57204923718
CreatorName
Fujii, Keisuke
Abstract
Many quantum algorithms, such as the 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, whereas the latter is required to perform arithmetics on a quantum computer. Quantum algorithms such as HHL achieve quantum speedups with a sophisticated use of these two encodings. In this work, we present algorithms that convert 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 a 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 amplitudes of a quantum state. As an example, we construct a "quantum amplitude perceptron," a quantum version of the neural network that hence has a possible application in the area of quantum machine learning.
Publisher
American Physical Society
Source Title
Physical Review A
Volume (Issue)
99(1)
Page
012301
Date of Issued
2019-01-02
Language
English
Handle URL
https://hdl.handle.net/11094/77644
PISSN
24699926
eISSN
24699934
NCID
AA10764867
Relation.isIdenticalTo
DOI (Publisher Version)
https://doi.org/10.1103/PhysRevA.99.012301
Access Rights
open access
CopyRight
Copyright (2019) by the American Physical Society
oaire:version
Version of Record
Category
学術雑誌論文 Journal Article