Storage and retrieval of von Neumann measurements via indefinite causal order structures

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27740%2F24%3A10255733" target="_blank" >RIV/61989100:27740/24:10255733 - isvavai.cz</a>

Výsledek na webu

<a href="https://journals.aps.org/pra/abstract/10.1103/PhysRevA.110.042422" target="_blank" >https://journals.aps.org/pra/abstract/10.1103/PhysRevA.110.042422</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1103/PhysRevA.110.042422" target="_blank" >10.1103/PhysRevA.110.042422</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Storage and retrieval of von Neumann measurements via indefinite causal order structures

Popis výsledku v původním jazyce

This work presents the problem of learning an unknown von Neumann measurement of dimension ???? using indefinite causal structures. In the considered scenario, we have access to ???? copies of the measurement. We use the formalism of process matrices to store information about the given measurement that later will be used to reproduce its best possible approximation. Our goal is to compute the maximum value of the average fidelity function ????????(????) of our procedure. We prove that ????????(????)=1MINUS SIGN Θ(1????2 ) for arbitrary but fixed dimension ????. Furthermore, we present the SDP program (semi-definite program) for computing ????????(????). Basing on the numerical investigation, we show that for the qubit von Neumann measurements using indefinite causal learning structures provide better approximation than quantum networks, starting from ????GREATER-THAN OR EQUAL TO3.

Název v anglickém jazyce

Storage and retrieval of von Neumann measurements via indefinite causal order structures

Popis výsledku anglicky

This work presents the problem of learning an unknown von Neumann measurement of dimension ???? using indefinite causal structures. In the considered scenario, we have access to ???? copies of the measurement. We use the formalism of process matrices to store information about the given measurement that later will be used to reproduce its best possible approximation. Our goal is to compute the maximum value of the average fidelity function ????????(????) of our procedure. We prove that ????????(????)=1MINUS SIGN Θ(1????2 ) for arbitrary but fixed dimension ????. Furthermore, we present the SDP program (semi-definite program) for computing ????????(????). Basing on the numerical investigation, we show that for the qubit von Neumann measurements using indefinite causal learning structures provide better approximation than quantum networks, starting from ????GREATER-THAN OR EQUAL TO3.

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

—

Návaznosti

—

Rok uplatnění

2024

Kód důvěrnosti údajů

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

Název periodika

Physical Review A

ISSN

2469-9926

e-ISSN

2469-9934

Svazek periodika

110

Číslo periodika v rámci svazku

4

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

6

Strana od-do

—

Kód UT WoS článku

001340811100002

EID výsledku v databázi Scopus

2-s2.0-85208023251