A quantum causal discovery algorithm

Giarmatzi, C; Costa, F

A quantum causal discovery algorithm

Giarmatzi, C

Costa, F

Permalink

Publisher:: NATURE PUBLISHING GROUP
Publication Type:: Journal Article
Citation:: npj Quantum Information, 2018, 4, (1)
Issue Date:: 2018-12-01

Open Access

Copyright Clearance Process

Recently Added
In Progress
Open Access

This item is open access.

Adobe PDF

Download Published versionAdobe PDF (1.23 MB)

View on publisher's site

View statistics

Full metadata record

Field	Value	Language
dc.contributor.author	Giarmatzi, C https://orcid.org/0000-0001-8254-2169
dc.contributor.author	Costa, F
dc.date.accessioned	2022-09-07T22:05:32Z
dc.date.available	2022-09-07T22:05:32Z
dc.date.issued	2018-12-01
dc.identifier.citation	npj Quantum Information, 2018, 4, (1)
dc.identifier.issn	2056-6387
dc.identifier.issn	2056-6387
dc.identifier.uri	http://hdl.handle.net/10453/161491
dc.description.abstract	Finding a causal model for a set of classical variables is now a well-established task—but what about the quantum equivalent? Even the notion of a quantum causal model is controversial. Here, we present a causal discovery algorithm for quantum systems. The input to the algorithm is a process matrix describing correlations between quantum events. Its output consists of different levels of information about the underlying causal model. Our algorithm determines whether the process is causally ordered by grouping the events into causally ordered non-signaling sets. It detects if all relevant common causes are included in the process, which we label Markovian, or alternatively if some causal relations are mediated through some external memory. For a Markovian process, it outputs a causal model, namely the causal relations and the corresponding mechanisms, represented as quantum states and channels. Our algorithm opens the route to more general quantum causal discovery methods.
dc.language	English
dc.publisher	NATURE PUBLISHING GROUP
dc.relation.ispartof	npj Quantum Information
dc.relation.isbasedon	10.1038/s41534-018-0062-6
dc.rights	info:eu-repo/semantics/openAccess
dc.title	A quantum causal discovery algorithm
dc.type	Journal Article
utslib.citation.volume	4
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	2022-09-07T22:05:31Z
pubs.issue	1
pubs.publication-status	Published
pubs.volume	4
utslib.citation.issue	1

Abstract:

Finding a causal model for a set of classical variables is now a well-established task—but what about the quantum equivalent? Even the notion of a quantum causal model is controversial. Here, we present a causal discovery algorithm for quantum systems. The input to the algorithm is a process matrix describing correlations between quantum events. Its output consists of different levels of information about the underlying causal model. Our algorithm determines whether the process is causally ordered by grouping the events into causally ordered non-signaling sets. It detects if all relevant common causes are included in the process, which we label Markovian, or alternatively if some causal relations are mediated through some external memory. For a Markovian process, it outputs a causal model, namely the causal relations and the corresponding mechanisms, represented as quantum states and channels. Our algorithm opens the route to more general quantum causal discovery methods.

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

http://hdl.handle.net/10453/161491