Efficient online quantum state estimation using a matrix-exponentiated gradient method

Youssry, A; Ferrie, C; Tomamichel, M

Efficient online quantum state estimation using a matrix-exponentiated gradient method

Youssry, A Ferrie, C

Tomamichel, M

Permalink

Publication Type:: Journal Article
Citation:: New Journal of Physics, 2019, 21 (3)
Issue Date:: 2019-03-15

Recently Added

	Filename	Description	Size
	Youssry_2019_New_J._Phys._21_033006.pdf	Published Version	1.36 MB	Adobe PDF	View/Open

Copyright Clearance Process

Recently Added
In Progress
Open Access

This item is new to OPUS and is not currently available.

Full metadata record

Field	Value	Language
dc.contributor.author	Youssry, A	en_US
dc.contributor.author	Ferrie, C https://orcid.org/0000-0003-2736-9943	en_US
dc.contributor.author	Tomamichel, M https://orcid.org/0000-0001-5410-3329	en_US
dc.date.accessioned	2020-03-16T00:57:15Z
dc.date.available	2020-03-16T00:57:15Z
dc.date.issued	2019-03-15	en_US
dc.identifier.citation	New Journal of Physics, 2019, 21 (3)	en_US
dc.identifier.issn	1367-2630	en_US
dc.identifier.uri	http://hdl.handle.net/10453/139317
dc.description.abstract	© 2019 The Author(s). Published by IOP Publishing Ltd on behalf of the Institute of Physics and Deutsche Physikalische Gesellschaft. In this paper, we explore an efficient online algorithm for quantum state estimation based on a matrix-exponentiated gradient method previously used in the context of machine learning. The state update is governed by a learning rate that determines how much weight is given to the new measurement results obtained in each step. We show convergence of the running state estimate in probability to the true state for both noiseless and noisy measurements. We find that in the latter case the learning rate has to be chosen adaptively and decreasing to guarantee convergence beyond the noise threshold. As a practical alternative we then propose to use running averages of the measurement statistics and a constant learning rate to overcome the noise problem. The proposed algorithm is numerically compared with batch maximum-likelihood and least-squares estimators. The results show a superior performance of the new algorithm in terms of accuracy and runtime complexity.	en_US
dc.relation	http://purl.org/au-research/grants/arc/DE170100421
dc.relation	http://purl.org/au-research/grants/arc/DE160100821
dc.relation.ispartof	New Journal of Physics	en_US
dc.relation.isbasedon	10.1088/1367-2630/ab0438	en_US
dc.rights	'This is an author-created, un-copyedited version of an article accepted for publication in [New Journal of Physics]. IOP Publishing Ltd is not responsible for any errors or omissions in this version of the manuscript or any version derived from it. The definitive publisher-authenticated version is available online at [ihttps://iopscience.iop.org/article/10.1088/1367-2630/ab0438].'
dc.rights	info:eu-repo/semantics/restrictedAccess
dc.subject.classification	Fluids & Plasmas	en_US
dc.title	Efficient online quantum state estimation using a matrix-exponentiated gradient method	en_US
dc.type	Journal Article
utslib.citation.volume	3	en_US
utslib.citation.volume	21	en_US
utslib.for	02 Physical Sciences	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 Computer Science
pubs.organisational-group	/University of Technology Sydney/Strength - QSI - Centre for Quantum Software and Information
pubs.organisational-group	/University of Technology Sydney/Students
utslib.copyright.status	recently_added	*
pubs.issue	3	en_US
pubs.publication-status	Published	en_US
pubs.volume	21	en_US

Abstract:

© 2019 The Author(s). Published by IOP Publishing Ltd on behalf of the Institute of Physics and Deutsche Physikalische Gesellschaft. In this paper, we explore an efficient online algorithm for quantum state estimation based on a matrix-exponentiated gradient method previously used in the context of machine learning. The state update is governed by a learning rate that determines how much weight is given to the new measurement results obtained in each step. We show convergence of the running state estimate in probability to the true state for both noiseless and noisy measurements. We find that in the latter case the learning rate has to be chosen adaptively and decreasing to guarantee convergence beyond the noise threshold. As a practical alternative we then propose to use running averages of the measurement statistics and a constant learning rate to overcome the noise problem. The proposed algorithm is numerically compared with batch maximum-likelihood and least-squares estimators. The results show a superior performance of the new algorithm in terms of accuracy and runtime complexity.

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

http://hdl.handle.net/10453/139317