Practical adaptive quantum tomography

Granade, C; Ferrie, C; Flammia, ST

Practical adaptive quantum tomography

Granade, C Ferrie, C

Flammia, ST

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Publication Type:: Journal Article
Citation:: New Journal of Physics, 2017, 19 (11)
Issue Date:: 2017-11-01

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Field	Value	Language
dc.contributor.author	Granade, C	en_US
dc.contributor.author	Ferrie, C https://orcid.org/0000-0003-2736-9943	en_US
dc.contributor.author	Flammia, ST	en_US
dc.date.issued	2017-11-01	en_US
dc.identifier.citation	New Journal of Physics, 2017, 19 (11)	en_US
dc.identifier.issn	1367-2630	en_US
dc.identifier.uri	http://hdl.handle.net/10453/124678
dc.description.abstract	© 2017 IOP Publishing Ltd and Deutsche Physikalische Gesellschaft. We introduce a fast and accurate heuristic for adaptive tomography that addresses many of the limitations of prior methods. Previous approaches were either too computationally intensive or tailored to handle special cases such as single qubits or pure states. By contrast, our approach combines the efficiency of online optimization with generally applicable and well-motivated data-processing techniques. We numerically demonstrate these advantages in several scenarios including mixed states, higher-dimensional systems, and restricted measurements.	en_US
dc.relation.ispartof	New Journal of Physics	en_US
dc.relation.isbasedon	10.1088/1367-2630/aa8fe6	en_US
dc.subject.classification	Fluids & Plasmas	en_US
dc.title	Practical adaptive quantum tomography	en_US
dc.type	Journal Article
utslib.citation.volume	11	en_US
utslib.citation.volume	19	en_US
utslib.for	0203 Classical Physics	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/Strength - QSI - Centre for Quantum Software and Information
utslib.copyright.status	open_access
pubs.issue	11	en_US
pubs.publication-status	Published	en_US
pubs.volume	19	en_US

Abstract:

© 2017 IOP Publishing Ltd and Deutsche Physikalische Gesellschaft. We introduce a fast and accurate heuristic for adaptive tomography that addresses many of the limitations of prior methods. Previous approaches were either too computationally intensive or tailored to handle special cases such as single qubits or pure states. By contrast, our approach combines the efficiency of online optimization with generally applicable and well-motivated data-processing techniques. We numerically demonstrate these advantages in several scenarios including mixed states, higher-dimensional systems, and restricted measurements.

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http://hdl.handle.net/10453/124678