arXiv：cs.LG· Melek Krichen, Nikhitha Nunavath, Riccardo Bassoli, Soumaya Cherkaoui, Frank H. P. Fitzek·· 6 小时前AI 评分28
基于变分量子神经网络的端到端量子语义通信
End-to-End Quantum Semantic Communication with Variational Quantum Neural Networks
AI 导读
该研究将量子机器学习与量子语义通信整合进端到端学习框架，用变分量子发射机学习任务相关语义特征并经由量子信道传输，接收端完成下游分类任务。在 MNIST 数据集和变分量子神经网络上，理想信道训练的基线模型在去极化噪声增强时不重训即评估，联合训练收发端则可自适应信道损伤并保留任务相关信息。
正文
Abstract: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.
| Subjects: | Quantum Physics (quant-ph); Machine Learning (cs.LG) |
| Cite as: | arXiv:2609.25044 [quant-ph] |
| (or arXiv:2609.25044v2 [quant-ph] for this version) | |
| https://doi.org/10.48550/arXiv.2609.25044 arXiv-issued DOI via DataCite |
Submission history
From: Melek Krichen [view email]
[v1]
Sun, 30 Aug 2026 21:31:32 UTC (3,986 KB)
[v2]
Tue, 6 Oct 2026 19:31:37 UTC (4,133 KB)
来源：arXiv：cs.LG · arxiv.org