The quantum Fourier transform based on quantum vision representation

Li, HS; Fan, P; Xia, HY; Song, S; He, X

The quantum Fourier transform based on quantum vision representation

Li, HS Fan, P Xia, HY Song, S He, X

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Publication Type:: Journal Article
Citation:: Quantum Information Processing, 2018, 17 (12)
Issue Date:: 2018-12-01

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Field	Value	Language
dc.contributor.author	Li, HS	en_US
dc.contributor.author	Fan, P	en_US
dc.contributor.author	Xia, HY	en_US
dc.contributor.author	Song, S	en_US
dc.contributor.author	He, X https://orcid.org/0000-0001-8962-540X	en_US
dc.date.issued	2018-12-01	en_US
dc.identifier.citation	Quantum Information Processing, 2018, 17 (12)	en_US
dc.identifier.issn	1570-0755	en_US
dc.identifier.uri	http://hdl.handle.net/10453/129013
dc.description.abstract	© 2018, Springer Science+Business Media, LLC, part of Springer Nature. Quantum Fourier transform (QFT) plays a key role in many quantum algorithms, but the existing circuits of QFT are incomplete and lacking the proof of correctness. Furthermore, it is difficult to apply QFT to the concrete field of information processing. Thus, we firstly investigate quantum vision representation (QVR) and develop a model of QVR. Then, we design four complete circuits of QFT and inverse QFT and describe the functions of their components. Meanwhile, we prove the correctness of the four complete circuits using formula derivation. Next, 2D QFT and 3D QFT based on QVR are proposed for the first time. Experimental results with simulation show the proposed QFTs are valid and useful in processing quantum images and videos. In conclusion, this paper develops a complete framework of QFT based on QVR and provides a feasible scheme for QFT to be applied in quantum vision information processing.	en_US
dc.relation.ispartof	Quantum Information Processing	en_US
dc.relation.isbasedon	10.1007/s11128-018-2096-2	en_US
dc.rights	info:eu-repo/semantics/openAccess
dc.subject.classification	Mathematical Physics	en_US
dc.title	The quantum Fourier transform based on quantum vision representation	en_US
dc.type	Journal Article
utslib.citation.volume	12	en_US
utslib.citation.volume	17	en_US
utslib.for	0105 Mathematical Physics	en_US
utslib.for	0206 Quantum Physics	en_US
utslib.for	0802 Computation Theory and Mathematics	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering
pubs.organisational-group	/University of Technology Sydney/Strength - CRIN - Realtime Information Networks
pubs.organisational-group	/University of Technology Sydney/Strength - GBDTC - Global Big Data Technologies
utslib.copyright.status	open_access	*
pubs.issue	12	en_US
pubs.publication-status	Published	en_US
pubs.volume	17	en_US

Abstract:

© 2018, Springer Science+Business Media, LLC, part of Springer Nature. Quantum Fourier transform (QFT) plays a key role in many quantum algorithms, but the existing circuits of QFT are incomplete and lacking the proof of correctness. Furthermore, it is difficult to apply QFT to the concrete field of information processing. Thus, we firstly investigate quantum vision representation (QVR) and develop a model of QVR. Then, we design four complete circuits of QFT and inverse QFT and describe the functions of their components. Meanwhile, we prove the correctness of the four complete circuits using formula derivation. Next, 2D QFT and 3D QFT based on QVR are proposed for the first time. Experimental results with simulation show the proposed QFTs are valid and useful in processing quantum images and videos. In conclusion, this paper develops a complete framework of QFT based on QVR and provides a feasible scheme for QFT to be applied in quantum vision information processing.

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http://hdl.handle.net/10453/129013