Routing in quantum communication networks using reinforcement machine learning

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68378271%3A_____%2F24%3A00602067" target="_blank" >RIV/68378271:_____/24:00602067 - isvavai.cz</a>
Alternative codes found
RIV/61989592:15310/24:73625885
Result on the web
<a href="https://hdl.handle.net/11104/0359362" target="_blank" >https://hdl.handle.net/11104/0359362</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/s11128-024-04287-z" target="_blank" >10.1007/s11128-024-04287-z</a>

Result language
angličtina
Original language name
Routing in quantum communication networks using reinforcement machine learning
Original language description
This paper promotes reinforcement machine learning for route-finding tasks in quantum communication networks, where, due to the non-additivity of quantum errors, classical graph path or tree-finding algorithms cannot be used. We propose using a proximal policy optimization algorithm capable of finding routes in teleportation-based quantum networks. This algorithm is benchmarked against the Monte Carlo search. The topology of our network resembles the proposed 6 G topology and analyzed that quantum errors correspond to typical errors in realistic quantum channels.
Czech name
—
Czech description
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Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
10306 - Optics (including laser optics and quantum optics)

Project
Result was created during the realization of more than one project. More information in the Projects tab.
Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year
2024
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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