A hybrid quantum-classical Hamiltonian learning algorithm

Wang, Y; Li, G; Wang, X

A hybrid quantum-classical Hamiltonian learning algorithm

Wang, Y Li, G Wang, X

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Publisher:: Springer Nature
Publication Type:: Journal Article
Citation:: Science China Information Sciences, 2023, 66, (2), pp. 129502
Issue Date:: 2023-02-01

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Field	Value	Language
dc.contributor.author	Wang, Y
dc.contributor.author	Li, G
dc.contributor.author	Wang, X
dc.date.accessioned	2024-02-01T03:14:59Z
dc.date.available	2024-02-01T03:14:59Z
dc.date.issued	2023-02-01
dc.identifier.citation	Science China Information Sciences, 2023, 66, (2), pp. 129502
dc.identifier.issn	1674-733X
dc.identifier.issn	1869-1919
dc.identifier.uri	http://hdl.handle.net/10453/175201
dc.description.abstract	This study develops a concrete near-term quantum algorithm for Hamiltonian learning and demonstrates its effectiveness. In particular, we show that learning the spectrum of Hamiltonians during the learning process could produce high-precision estimates of the target interaction coefficients. Our work may have applications in quantum device certification, quantum simulation, and quantum machine learning.
dc.language	en
dc.publisher	Springer Nature
dc.relation	http://purl.org/au-research/grants/arc/DP180100691
dc.relation.ispartof	Science China Information Sciences
dc.relation.isbasedon	10.1007/s11432-021-3382-2
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0804 Data Format, 0806 Information Systems, 0899 Other Information and Computing Sciences
dc.subject.classification	Software Engineering
dc.subject.classification	4006 Communications engineering
dc.subject.classification	4007 Control engineering, mechatronics and robotics
dc.subject.classification	4009 Electronics, sensors and digital hardware
dc.title	A hybrid quantum-classical Hamiltonian learning algorithm
dc.type	Journal Article
utslib.citation.volume	66
utslib.for	0804 Data Format
utslib.for	0806 Information Systems
utslib.for	0899 Other Information and Computing Sciences
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 Computer Science
utslib.copyright.status	closed_access	*
dc.date.updated	2024-02-01T03:14:58Z
pubs.issue	2
pubs.publication-status	Published
pubs.volume	66
utslib.citation.issue	2

Abstract:

This study develops a concrete near-term quantum algorithm for Hamiltonian learning and demonstrates its effectiveness. In particular, we show that learning the spectrum of Hamiltonians during the learning process could produce high-precision estimates of the target interaction coefficients. Our work may have applications in quantum device certification, quantum simulation, and quantum machine learning.

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

http://hdl.handle.net/10453/175201