Quantum transfer component analysis for domain adaptation

He, X; Lyu, C; Hsieh, M-H; Wang, X

Quantum transfer component analysis for domain adaptation

He, X Lyu, C Hsieh, M-H Wang, X

Permalink

Publication Type:: Journal Article
Citation:: 2019
Issue Date:: 2019-12-19

Open Access

Copyright Clearance Process

Recently Added
In Progress
Open Access

This item is open access.

Adobe PDF

Download full textAdobe PDF (6.03 MB)

View statistics

Full metadata record

Field	Value	Language
dc.contributor.author	He, X
dc.contributor.author	Lyu, C
dc.contributor.author	Hsieh, M-H
dc.contributor.author	Wang, X
dc.date.accessioned	2023-03-13T23:06:26Z
dc.date.available	2023-03-13T23:06:26Z
dc.date.issued	2019-12-19
dc.identifier.citation	2019
dc.identifier.uri	http://hdl.handle.net/10453/167222
dc.description.abstract	Domain adaptation, a crucial sub-field of transfer learning, aims to utilize known knowledge of one data set to accomplish tasks on another data set. In this paper, we perform one of the most representative domain adaptation algorithms, transfer component analysis (TCA), on quantum devices. Two different quantum implementations of this transfer learning algorithm; namely, the linear-algebra-based quantum TCA algorithm and the variational quantum TCA algorithm, are presented. The algorithmic complexity of the linear-algebra-based quantum TCA algorithm is $O(\mathrm{poly}(\log (n_{s} + n_{t})))$, where $n_{s}$ and $n_{t}$ are input sample size. Compared with the corresponding classical algorithm, the linear-algebra-based quantum TCA can be performed on a universal quantum computer with exponential speedup in the number of given samples. Finally, the variational quantum TCA algorithm based on a quantum-classical hybrid procedure, that can be implemented on the near term quantum devices, is proposed.
dc.language	en
dc.rights	info:eu-repo/semantics/openAccess
dc.title	Quantum transfer component analysis for domain adaptation
dc.type	Journal Article
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/Strength - QSI - Centre for Quantum Software and Information
utslib.copyright.status	open_access	*
dc.date.updated	2023-03-13T23:06:14Z

Abstract:

Domain adaptation, a crucial sub-field of transfer learning, aims to utilize known knowledge of one data set to accomplish tasks on another data set. In this paper, we perform one of the most representative domain adaptation algorithms, transfer component analysis (TCA), on quantum devices. Two different quantum implementations of this transfer learning algorithm; namely, the linear-algebra-based quantum TCA algorithm and the variational quantum TCA algorithm, are presented. The algorithmic complexity of the linear-algebra-based quantum TCA algorithm is $O(\mathrm{poly}(\log (n_{s} + n_{t})))$, where $n_{s}$ and $n_{t}$ are input sample size. Compared with the corresponding classical algorithm, the linear-algebra-based quantum TCA can be performed on a universal quantum computer with exponential speedup in the number of given samples. Finally, the variational quantum TCA algorithm based on a quantum-classical hybrid procedure, that can be implemented on the near term quantum devices, is proposed.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/167222