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...

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Bibliographic Details
Published in:International Conference on Communication Systems and Networks (Online) pp. 988 - 993
Main Authors: Rathi, Lakshika, DiAdamo, Stephen, Shabani, Alireza
Format: Conference Proceeding
Language:English
Published: IEEE 03.01.2024
Subjects:
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
Online Access:Get full text
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Summary: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