Experimental characterisation of a non-Markovian quantum process

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

Experimental characterisation of a non-Markovian quantum process

Goswami, K Giarmatzi, C

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

Permalink

Publication Type:: Journal Article
Citation:: 2021
Issue Date:: 2021-02-02

Open Access

Copyright Clearance Process

Recently Added
In Progress
Open Access

This item is open access.

Adobe PDF

Download full textAdobe PDF (567.13 kB)

View statistics

Full metadata record

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	2021-04-06T00:31:21Z
dc.date.available	2021-04-06T00:31:21Z
dc.date.issued	2021-02-02
dc.identifier.citation	2021
dc.identifier.uri	http://hdl.handle.net/10453/147844
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 characterisation of non-Markovian noise requires tomography of a multi-time 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.rights	info:eu-repo/semantics/openAccess
dc.title	Experimental characterisation of a non-Markovian quantum process
dc.type	Journal Article
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
utslib.copyright.status	open_access	*
dc.date.updated	2021-04-06T00:31:18Z

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 characterisation of non-Markovian noise requires tomography of a multi-time 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.

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

http://hdl.handle.net/10453/147844