Quantum speedup in the identification of cause-effect relations.


Journal

Nature communications
ISSN: 2041-1723
Titre abrégé: Nat Commun
Pays: England
ID NLM: 101528555

Informations de publication

Date de publication:
01 04 2019
Historique:
received: 19 07 2018
accepted: 08 03 2019
entrez: 2 4 2019
pubmed: 2 4 2019
medline: 2 4 2019
Statut: epublish

Résumé

The ability to identify cause-effect relations is an essential component of the scientific method. The identification of causal relations is generally accomplished through statistical trials where alternative hypotheses are tested against each other. Traditionally, such trials have been based on classical statistics. However, classical statistics becomes inadequate at the quantum scale, where a richer spectrum of causal relations is accessible. Here we show that quantum strategies can greatly speed up the identification of causal relations. We analyse the task of identifying the effect of a given variable, and we show that the optimal quantum strategy beats all classical strategies by running multiple equivalent tests in a quantum superposition. The same working principle leads to advantages in the detection of a causal link between two variables, and in the identification of the cause of a given variable.

Identifiants

pubmed: 30931930
doi: 10.1038/s41467-019-09383-8
pii: 10.1038/s41467-019-09383-8
pmc: PMC6443664
doi:

Types de publication

Journal Article Research Support, Non-U.S. Gov't

Langues

eng

Pagination

1472

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Auteurs

Giulio Chiribella (G)

Department of Computer Science, The University of Hong Kong, Pokfulam Road, Hong Kong. giulio@cs.hku.hk.
Department of Computer Science, University of Oxford, Oxford, OX1 3QD, UK. giulio@cs.hku.hk.
Perimeter Institute for Theoretical Physics, Waterloo, ON, N2L 2Y5, Canada. giulio@cs.hku.hk.

Daniel Ebler (D)

Department of Computer Science, The University of Hong Kong, Pokfulam Road, Hong Kong.
Department of Physics, Institute for Quantum Science and Engineering, Southern University of Science and Technology, Shenzhen, 518055, China.

Classifications MeSH