Experimental measurement of the Hilbert-Schmidt distance between two-qubit states as a means for reducing the complexity of machine learning

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68378271%3A_____%2F19%3A00520175" target="_blank" >RIV/68378271:_____/19:00520175 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/61989592:15310/19:73597417

Výsledek na webu

<a href="https://doi.org/10.1103/physrevlett.123.260501" target="_blank" >https://doi.org/10.1103/physrevlett.123.260501</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1103/PhysRevLett.123.260501" target="_blank" >10.1103/PhysRevLett.123.260501</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Experimental measurement of the Hilbert-Schmidt distance between two-qubit states as a means for reducing the complexity of machine learning

Popis výsledku v původním jazyce

We report on the experimental measurement of the Hilbert-Schmidt distance between two two-qubit states by many-particle interference. We demonstrate that our three-step method for measuring distances in the Hilbert space is far less complex than reconstructing density matrices and that it can be applied in quantum-enhanced machine learning to reduce the complexity of calculating Euclidean distances between multidimensional points, which can be especially interesting for near term quantum technologies and quantum artificial intelligence research. Our results are also a novel example of applying mixed states in quantum information processing. Usually working with mixed states is undesired, but here it gives the possibility of encoding extra information as the degree of coherence between the given two dimensions of the density matrix.n

Název v anglickém jazyce

Experimental measurement of the Hilbert-Schmidt distance between two-qubit states as a means for reducing the complexity of machine learning

Popis výsledku anglicky

We report on the experimental measurement of the Hilbert-Schmidt distance between two two-qubit states by many-particle interference. We demonstrate that our three-step method for measuring distances in the Hilbert space is far less complex than reconstructing density matrices and that it can be applied in quantum-enhanced machine learning to reduce the complexity of calculating Euclidean distances between multidimensional points, which can be especially interesting for near term quantum technologies and quantum artificial intelligence research. Our results are also a novel example of applying mixed states in quantum information processing. Usually working with mixed states is undesired, but here it gives the possibility of encoding extra information as the degree of coherence between the given two dimensions of the density matrix.n

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

Výsledek vznikl pri realizaci vícero projektů. Více informací v záložce Projekty.

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2019

Kód důvěrnosti údajů

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

Název periodika

Physical Review Letters

ISSN

0031-9007

e-ISSN

—

Svazek periodika

123

Číslo periodika v rámci svazku

26

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

6

Strana od-do

1-6

Kód UT WoS článku

000504647000002

EID výsledku v databázi Scopus

2-s2.0-85077334785