Quantum Autoencoders for Learning Quantum Channel Codes
This advances quantum communication systems by enabling better understanding of capacity bounds under constraints, though it appears incremental as it applies existing quantum ML techniques to channel coding.
This work tackles the problem of generating quantum channel codes for classical and quantum communication by developing a quantum machine learning framework using parameterized quantum circuits and flexible noise models, demonstrating strong performance across various channel models as proof of concept.
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.