用于端到端无线通信的混合量子经典自编码器
Hybrid Quantum-Classical Autoencoders for End-to-End Radio Communication
摘要：
量子神经网络正在成为利用噪声量子处理单元进行应用的潜在候选者。在这里，我们将介绍用于端到端无线电通信的混合量子经典自编码器。在经典无线系统的物理层中，我们研究了标准编码无线电信号在噪声信道上的模拟架构的性能。我们实现了一个混合模型，在接收端的量子解码器与发送端的经典编码器一起工作。除了学习输入符号的潜在空间表示，具有良好的鲁棒性，对信号退化，基于量子位的电路的广义数据重新上传方案允许满足应用程序的推理时间约束。
英文摘要：
Quantum neural networks are emerging as potential candidates to leverage noisy quantum processing units for applications. Here we introduce hybrid quantum-classical autoencoders for end-to-end radio communication. In the physical layer of classical wireless systems, we study the performance of simulated architectures for standard encoded radio signals over a noisy channel. We implement a hybrid model, where a quantum decoder in the receiver works with a classical encoder in the transmitter part. Besides learning a latent space representation of the input symbols with good robustness against signal degradation, a generalized data re-uploading scheme for the qubit-based circuits allows to meet inference-time constraints of the application.
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