Vorträge

Vortrag Professor Julio de Vicente, 17:00, room 2501 PH-I

VORTRÄGE/TALKS |

Vortrag Professor Julio de Vicente, Universidad Complutense Madrid, on July 17 2023, 5:00 p.m. , in Rudolf-Mößbauer-Hörsaal 2501 PH-I

Gastvortrag von Prof. Julio de Vicente

 Departamento de Matemáticas at Universidad Carlos III de Madrid.

am 17. Juli 2023, 17:00 Uhr, im Rudolf-Mößbauer-Hörsaal 2501 PH-I

"Asymptomatic robustness of genuine multipartite entanglement in noisy quentum netowrks"

 

The study of entanglement in multipartite quantum states plays a major role in quantum
information theory and genuine multipartite entanglement (GME) signals one of its
strongest forms for applications.
However, its characterization is a highly nontrivial problem and its experimental preparation
faces the formidable challenge of controlling quantum states with many constituents.
Quantum networks (that arise by distributing exclusively bipartite entanglement among
given pairs of parties) represent a particularly feasible way to prepare multipartite entangled
states and are now being actively investigated as a platform for quantum information tasks.
In this talk we study the ability of quantum networks to display GME when the distributed
bipartite entanglement becomes noisy (i.e. mixed).
We will first observe that GME in networks depends on both the level of noise and the
network topology and, in sharp contrast to the case of pure states, it is not guaranteed by
the mere distribution of mixed bipartite entangled states.
More importantly, we will show that there is a markedly drastic feature: for some network
configurations GME is robust to noise for any system size while for others it is completely
washed out under the slightest form of noise for a sufficiently large number of parties.
This latter case implies fundamental limitations for the application of certain networks in
realistic scenarios, where the presence of some form of noise is unavoidable.
We will then discuss how different parameters measuring the degree of connectivity in a
network can characterize asymptotic survival of GME.