Deep-learned classification of quantum correlations from collective measurements

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68378271%3A_____%2F25%3A00639337" target="_blank" >RIV/68378271:_____/25:00639337 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/61989592:15310/25:73631810

Výsledek na webu

<a href="https://doi.org/10.1016/j.physleta.2025.130911" target="_blank" >https://doi.org/10.1016/j.physleta.2025.130911</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.physleta.2025.130911" target="_blank" >10.1016/j.physleta.2025.130911</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Deep-learned classification of quantum correlations from collective measurements

Popis výsledku v původním jazyce

The paper suggests employing deep artificial neural networks (ANNs) for resource-efficient classification of quantum correlations based on collective measurements conducted in the geometry of entanglement swapping. ANNs are trained to categorize two-qubit quantum states into five mutually exclusive classes depending on the strength of quantum correlations exhibited by these states. The precision and recall of the ANN models are analyzed as functions of the quantum resources consumed, i.e. the number of collective measurements or shots performed. The results indicate that the ANN models outperform analytical solutions, as they are capable of distinguishing between classes intractable by analytical witnesses, can operate under a reduced number of measurement configurations, and significantly better cope with detection noise and imperfect quantum channels.

Název v anglickém jazyce

Deep-learned classification of quantum correlations from collective measurements

Popis výsledku anglicky

The paper suggests employing deep artificial neural networks (ANNs) for resource-efficient classification of quantum correlations based on collective measurements conducted in the geometry of entanglement swapping. ANNs are trained to categorize two-qubit quantum states into five mutually exclusive classes depending on the strength of quantum correlations exhibited by these states. The precision and recall of the ANN models are analyzed as functions of the quantum resources consumed, i.e. the number of collective measurements or shots performed. The results indicate that the ANN models outperform analytical solutions, as they are capable of distinguishing between classes intractable by analytical witnesses, can operate under a reduced number of measurement configurations, and significantly better cope with detection noise and imperfect quantum channels.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10306 - Optics (including laser optics and quantum optics)

Projekt

<a href="/cs/project/GA25-17253S" target="_blank" >GA25-17253S: Kolektivní svědkové provázanosti umožňující nové objevy a aplikace</a><br>

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2025

Kód důvěrnosti údajů

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

Název periodika

Physics Letters. A

ISSN

0375-9601

e-ISSN

1873-2429

Svazek periodika

559

Číslo periodika v rámci svazku

Nov

Stát vydavatele periodika

NL - Nizozemsko

Počet stran výsledku

9

Strana od-do

130911

Kód UT WoS článku

001566004300001

EID výsledku v databázi Scopus

2-s2.0-105014758626