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Ultimate Limits for Quickest Quantum Change-Point Detection.
Fanizza, Marco; Hirche, Christoph; Calsamiglia, John.
Afiliação
  • Fanizza M; Física Teòrica: Informació i Fenòmens Quàntics, Departament de Física, Universitat Autònoma de Barcelona, ES-08193 Bellaterra (Barcelona), Spain.
  • Hirche C; Center for Quantum Technologies, National University of Singapore Zentrum Mathematik, Technical University of Munich, 85748 Garching, Germany.
  • Calsamiglia J; Física Teòrica: Informació i Fenòmens Quàntics, Departament de Física, Universitat Autònoma de Barcelona, ES-08193 Bellaterra (Barcelona), Spain.
Phys Rev Lett ; 131(2): 020602, 2023 Jul 14.
Article em En | MEDLINE | ID: mdl-37505971
ABSTRACT
Detecting abrupt changes in data streams is crucial because they are often triggered by events that have important consequences if left unattended. Quickest change-point detection has become a vital sequential analysis primitive that aims at designing procedures that minimize the expected detection delay of a change subject to a bounded expected false alarm time. We put forward the quantum counterpart of this fundamental primitive on streams of quantum data. We give a lower bound on the mean minimum delay when the expected time of a false alarm is asymptotically large, under the most general quantum detection strategy, which is given by a sequence of adaptive collective (potentially weak) measurements on the growing string of quantum data. In addition, we give particular strategies based on repeated measurements on independent blocks of samples that asymptotically attain the lower bound and thereby establish the ultimate quantum limit for quickest change-point detection. Finally, we discuss online change-point detection in quantum channels.

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Diagnostic_studies Idioma: En Revista: Phys Rev Lett Ano de publicação: 2023 Tipo de documento: Article País de afiliação: Espanha

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Diagnostic_studies Idioma: En Revista: Phys Rev Lett Ano de publicação: 2023 Tipo de documento: Article País de afiliação: Espanha