Sensitivity versus selectivity in entanglement detection via collective witnesses

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68378271%3A_____%2F24%3A00588243" target="_blank" >RIV/68378271:_____/24:00588243 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/61989592:15310/24:73626329

Výsledek na webu

<a href="https://hdl.handle.net/11104/0357501" target="_blank" >https://hdl.handle.net/11104/0357501</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1103/PhysRevResearch.6.033056" target="_blank" >10.1103/PhysRevResearch.6.033056</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Sensitivity versus selectivity in entanglement detection via collective witnesses

Popis výsledku v původním jazyce

In this paper, we introduce a supervised learning technique that harnesses artificial neural networks, along with the outcomes of collective entanglement measurements, to estimate the negativity of quantum states in two-qubit and qubit-qutrit systems. The resulting deep-learned collective entanglement witnesses offer the unique capability of continuous sensitivity and selectivity tuning. This instrument enables us to explore the tradeoff between sensitivity and selectivity in entanglement detection, a dimension not accessible to previously employed analytical witnesses. In particular, we demonstrate that there are experimentally cost-effective methods (in terms of the number of measurements) where sensitivity can be significantly improved at a slight expense of the selectivity of entanglement detection. This chosen approach is also favored due to its high generality and potential for superior performance compared to other types of entanglement witnesses. Our findings may pave the way for the development of more efficient and accurate entanglement detection methods in complex quantum systems, especially when considering realistic experimental imperfections.

Název v anglickém jazyce

Sensitivity versus selectivity in entanglement detection via collective witnesses

Popis výsledku anglicky

In this paper, we introduce a supervised learning technique that harnesses artificial neural networks, along with the outcomes of collective entanglement measurements, to estimate the negativity of quantum states in two-qubit and qubit-qutrit systems. The resulting deep-learned collective entanglement witnesses offer the unique capability of continuous sensitivity and selectivity tuning. This instrument enables us to explore the tradeoff between sensitivity and selectivity in entanglement detection, a dimension not accessible to previously employed analytical witnesses. In particular, we demonstrate that there are experimentally cost-effective methods (in terms of the number of measurements) where sensitivity can be significantly improved at a slight expense of the selectivity of entanglement detection. This chosen approach is also favored due to its high generality and potential for superior performance compared to other types of entanglement witnesses. Our findings may pave the way for the development of more efficient and accurate entanglement detection methods in complex quantum systems, especially when considering realistic experimental imperfections.

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 Research

ISSN

2643-1564

e-ISSN

2643-1564

Svazek periodika

6

Číslo periodika v rámci svazku

3

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

9

Strana od-do

033056

Kód UT WoS článku

001272310900011

EID výsledku v databázi Scopus

2-s2.0-85198613247