Quantum Autoencoders for Learning Quantum Channel Codes

This work investigates the application of quantum machine learning techniques for classical and quantum communication across different qubit channel models. By employing parameterized quantum circuits and a flexible channel noise model, we develop a machine learning framework to generate quantum cha...

Celý popis

Uloženo v:
Podrobná bibliografie
Vydáno v:International Conference on Communication Systems and Networks (Online) s. 988 - 993
Hlavní autoři: Rathi, Lakshika, DiAdamo, Stephen, Shabani, Alireza
Médium: Konferenční příspěvek
Jazyk:angličtina
Vydáno: IEEE 03.01.2024
Témata:
Channel coding
Channel models
classical-quantum communication
Codes
Integrated circuit modeling
Machine learning
quantum channel capacity
Quantum channels
quantum communication
quantum machine learning
Quantum mechanics
quantum Shannon theory
Qubit
ISSN:2155-2509
On-line přístup:Získat plný text
Tagy: Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
Popis
Shrnutí:This work investigates the application of quantum machine learning techniques for classical and quantum communication across different qubit channel models. By employing parameterized quantum circuits and a flexible channel noise model, we develop a machine learning framework to generate quantum channel codes and evaluate their effectiveness. We explore classical, entanglement-assisted, and quantum communication scenarios within our framework. Applying it to various quantum channel models as proof of concept, we demonstrate strong performance in each case. Our results highlight the potential of quantum machine learning in advancing research on quantum communication systems, enabling a better understanding of capacity bounds under modulation constraints, various communication settings, and diverse channel models.
ISSN:2155-2509
DOI:10.1109/COMSNETS59351.2024.10427450