Revealing nonclassicality of multiphoton optical beams via artificial neural networks

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68378271%3A_____%2F24%3A00598466" target="_blank" >RIV/68378271:_____/24:00598466 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/61989592:15310/24:73626520

Výsledek na webu

<a href="https://hdl.handle.net/11104/0356130" target="_blank" >https://hdl.handle.net/11104/0356130</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1103/PhysRevApplied.22.034048" target="_blank" >10.1103/PhysRevApplied.22.034048</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Revealing nonclassicality of multiphoton optical beams via artificial neural networks

Popis výsledku v původním jazyce

The identification of nonclassical features of multiphoton quantum states represents a task of the utmost importance in the development of many quantum photonic technologies. Under realistic experimental conditions, a photonic quantum state gets affected by its interaction with several nonideal opto-electronic devices, including those used to guide, detect or characterize it. The result of such noisy interaction is that the nonclassical features of the original quantum state get considerably reduced or are completely absent in the detected, final state. In this work, the self-learning features of artificial neural networks are exploited to experimentally show that the nonclassicality of multiphoton quantum states can be assessed and fully characterized, even in the cases in which the nonclassical features are concealed by the measuring devices. Our work paves the way toward artificial-intelligence-assisted experimental-setup characterization, as well as smart quantum-state nonclassicality identification.

Název v anglickém jazyce

Revealing nonclassicality of multiphoton optical beams via artificial neural networks

Popis výsledku anglicky

The identification of nonclassical features of multiphoton quantum states represents a task of the utmost importance in the development of many quantum photonic technologies. Under realistic experimental conditions, a photonic quantum state gets affected by its interaction with several nonideal opto-electronic devices, including those used to guide, detect or characterize it. The result of such noisy interaction is that the nonclassical features of the original quantum state get considerably reduced or are completely absent in the detected, final state. In this work, the self-learning features of artificial neural networks are exploited to experimentally show that the nonclassicality of multiphoton quantum states can be assessed and fully characterized, even in the cases in which the nonclassical features are concealed by the measuring devices. Our work paves the way toward artificial-intelligence-assisted experimental-setup characterization, as well as smart quantum-state nonclassicality identification.

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/EH22_008%2F0004596" target="_blank" >EH22_008/0004596: Senzory a detektory pro informační společnost budoucnosti</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2024

Kód důvěrnosti údajů

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

Název periodika

Physical Review Applied

ISSN

2331-7019

e-ISSN

2331-7019

Svazek periodika

22

Číslo periodika v rámci svazku

3

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

11

Strana od-do

034048

Kód UT WoS článku

001329205400001

EID výsledku v databázi Scopus

2-s2.0-85204797914