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.

本研究探讨了量子机器学习技术在经典和量子通信中的应用，证明了该技术在不同量子信道模型下的强大性能，并为量子通信系统的研究提供了新的进展，使得人们在调制约束、各种通信设置和多样化的信道模型的容量界限方面有了更好的理解。