发表机构
Polytechnique Montréal; TU Dresden（蒙特利尔理工学院; 德累斯顿工业大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出一种结合量子机器学习与语义通信的端到端量子语义通信框架，通过变分量子发射器和可训练接收器在噪声信道下实现分类，实验表明接收器训练能有效恢复语义信息并保持高精度。
AI 中文摘要
本文提出了一种结合量子机器学习（QML）和语义通信（SemCom）的量子语义通信（QSemCom）框架。经典数据被压缩为低维语义表示，由变分量子发射器进行编码和处理，通过量子信道传输，并由可训练的量子接收器进行处理以进行分类。该框架考虑了一种分布式量子通信场景，其中量子处理单元（QPU）通过量子链路交换任务相关的语义信息。虽然一般设置可能涉及多个量子节点，但本工作聚焦于基本的两节点情形，即发射器和接收器QPU通过噪声量子信道连接。利用MNIST数据集，该框架在理想、比特翻转、退极化和振幅阻尼信道下进行了评估。首先在完美信道上训练基线模型，并在不重新训练的情况下评估其在不同噪声水平下的性能。随后在固定的退极化噪声水平下进行接收器端到端训练。完美信道模型的准确率达到$0.9556$，F1分数为$0.9551$。结果表明，性能随信道变化而下降，而接收器训练在中等和高退极化噪声下显著恢复了任务性能。此外，任务恢复不需要重建传输的密度矩阵，这凸显了物理状态恢复与语义特征恢复之间的区别。这些结果表明，可训练的量子接收器能够从噪声失真的量子态中恢复任务相关的语义信息，并保持较高的分类性能。
英文摘要
Quantum-enabled learning is increasingly being explored for future communication and networking applications, including distributed sensing, Internet of Things (IoT), and distributed quantum computing. However, existing approaches often face challenges in scalable inference and efficient information processing. To address these limitations, this paper integrates quantum machine learning (QML) with quantum semantic communication (QSemCom) in an end-to-end learning framework. A classical dataset is first mapped to a low-dimensional feature representation and encoded by a variational quantum transmitter that learns task-relevant semantic features. The resulting features are transmitted through a quantum channel and processed by a receiver to perform a downstream classification task. The proposed framework is evaluated using the MNIST dataset and variational quantum neural networks (QNNs) under ideal and noisy quantum-channel conditions. First, a baseline model is trained over an ideal channel and evaluated under increasing depolarizing noise without retraining. A second set of experiments introduces a trainable receiver QNN and jointly optimizes the transmitter and receiver through end-to-end (E2E) training. The results demonstrate that jointly trained quantum semantic transceivers can adapt the transmitted representation to channel impairments and preserve task-relevant information, highlighting the potential of receiver-aware QML for robust inference in quantum semantic communication.
