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.

翻译：本工作研究了量子机器学习技术在不同量子比特信道模型下用于经典通信与量子通信的应用。通过采用参数化量子电路和灵活的信道噪声模型，我们开发了一个机器学习框架，用于生成量子信道编码并评估其有效性。我们在该框架内探索了经典通信、纠缠辅助通信和量子通信场景。将其作为概念验证应用于各种量子信道模型，我们在每种情况下都展现了强大的性能。我们的结果凸显了量子机器学习在推进量子通信系统研究方面的潜力，能够更深入地理解调制约束下的容量界限、多种通信设置以及多样化的信道模型。