Bayesian recursive data-pattern tomography

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989592%3A15310%2F15%3A33154971" target="_blank" >RIV/61989592:15310/15:33154971 - isvavai.cz</a>

Výsledek na webu

<a href="http://journals.aps.org/pra/pdf/10.1103/PhysRevA.92.052106" target="_blank" >http://journals.aps.org/pra/pdf/10.1103/PhysRevA.92.052106</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1103/PhysRevA.92.052106" target="_blank" >10.1103/PhysRevA.92.052106</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Bayesian recursive data-pattern tomography

Popis výsledku v původním jazyce

We present a simple and efficient Bayesian recursive algorithm for the data-pattern scheme for quantum state reconstruction, which is applicable to situations where measurement settings can be controllably varied efficiently. The algorithm predicts the best measurements required to accurately reconstruct the unknown signal state in terms of a fixed set of probe states. In each iterative step, this algorithm seeks the measurement setting that minimizes the variance of the data-pattern estimator, which essentially measures the reconstruction accuracy, with the help of a data-pattern bank that was acquired prior to the signal reconstruction. We show that, with this algorithm, it is possible to minimize the number of measurement settings required to obtaina reasonably accurate state estimator by using just the optimal settings and, at the same time, increasing the numerical efficiency of the data-pattern reconstruction.

Název v anglickém jazyce

Bayesian recursive data-pattern tomography

Popis výsledku anglicky

We present a simple and efficient Bayesian recursive algorithm for the data-pattern scheme for quantum state reconstruction, which is applicable to situations where measurement settings can be controllably varied efficiently. The algorithm predicts the best measurements required to accurately reconstruct the unknown signal state in terms of a fixed set of probe states. In each iterative step, this algorithm seeks the measurement setting that minimizes the variance of the data-pattern estimator, which essentially measures the reconstruction accuracy, with the help of a data-pattern bank that was acquired prior to the signal reconstruction. We show that, with this algorithm, it is possible to minimize the number of measurement settings required to obtaina reasonably accurate state estimator by using just the optimal settings and, at the same time, increasing the numerical efficiency of the data-pattern reconstruction.

Druh

J_x - Nezařazeno - Článek v odborném periodiku (Jimp, Jsc a Jost)

CEP obor

BH - Optika, masery a lasery

OECD FORD obor

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Projekt

<a href="/cs/project/GA15-03194S" target="_blank" >GA15-03194S: Informačně úplná měření pro zpracování informace prostřednictvím náhodného světla</a><br>

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2015

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Physical Review A

ISSN

1050-2947

e-ISSN

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Svazek periodika

92

Číslo periodika v rámci svazku

5

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

10

Strana od-do

"052106-1"-"052106-10"

Kód UT WoS článku

000364396900002

EID výsledku v databázi Scopus

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