Experimental characterization of a non-Markovian quantum process

Goswami, K; Giarmatzi, C; Monterola, C; Shrapnel, S; Romero, J; Costa, F

Experimental characterization of a non-Markovian quantum process

Goswami, K Giarmatzi, C

Monterola, C Shrapnel, S Romero, J Costa, F

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Publisher:: American Physical Society (APS)
Publication Type:: Journal Article
Citation:: Physical Review A, 2021, 104, (2), pp. 022432
Issue Date:: 2021-08-01

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Field	Value	Language
dc.contributor.author	Goswami, K
dc.contributor.author	Giarmatzi, C https://orcid.org/0000-0001-8254-2169
dc.contributor.author	Monterola, C
dc.contributor.author	Shrapnel, S
dc.contributor.author	Romero, J
dc.contributor.author	Costa, F
dc.date.accessioned	2022-02-16T00:21:59Z
dc.date.available	2022-02-16T00:21:59Z
dc.date.issued	2021-08-01
dc.identifier.citation	Physical Review A, 2021, 104, (2), pp. 022432
dc.identifier.issn	2469-9926
dc.identifier.issn	2469-9934
dc.identifier.uri	http://hdl.handle.net/10453/154565
dc.description.abstract	Every quantum system is coupled to an environment. Such system-environment interaction leads to temporal correlation between quantum operations at different times, resulting in non-Markovian noise. In principle, a full characterization of non-Markovian noise requires tomography of a multitime processes matrix, which is both computationally and experimentally demanding. In this paper, we propose a more efficient solution. We employ machine learning models to estimate the amount of non-Markovianity, as quantified by an information-theoretic measure, with tomographically incomplete measurement. We test our model on a quantum optical experiment, and we are able to predict the non-Markovianity measure with 90% accuracy. Our experiment paves the way for efficient detection of non-Markovian noise appearing in large scale quantum computers.
dc.language	en
dc.publisher	American Physical Society (APS)
dc.relation	Sydney Quantum Academy
dc.relation.ispartof	Physical Review A
dc.relation.isbasedon	10.1103/PhysRevA.104.022432
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	01 Mathematical Sciences, 02 Physical Sciences, 03 Chemical Sciences
dc.subject.classification	General Physics
dc.title	Experimental characterization of a non-Markovian quantum process
dc.type	Journal Article
utslib.citation.volume	104
utslib.for	01 Mathematical Sciences
utslib.for	02 Physical Sciences
utslib.for	03 Chemical Sciences
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
utslib.copyright.status	closed_access	*
dc.date.updated	2022-02-16T00:21:58Z
pubs.issue	2
pubs.publication-status	Published
pubs.volume	104
utslib.citation.issue	2

Abstract:

Every quantum system is coupled to an environment. Such system-environment interaction leads to temporal correlation between quantum operations at different times, resulting in non-Markovian noise. In principle, a full characterization of non-Markovian noise requires tomography of a multitime processes matrix, which is both computationally and experimentally demanding. In this paper, we propose a more efficient solution. We employ machine learning models to estimate the amount of non-Markovianity, as quantified by an information-theoretic measure, with tomographically incomplete measurement. We test our model on a quantum optical experiment, and we are able to predict the non-Markovianity measure with 90% accuracy. Our experiment paves the way for efficient detection of non-Markovian noise appearing in large scale quantum computers.

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http://hdl.handle.net/10453/154565