End-to-End Quantum Semantic Communication with Variational Quantum Neural Networks
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.
Related
- Variational Functional Neural Networks (vfNN)
- QCMI-Based Quantum Markov Blanket Discovery for Semantic Quantum Networks
- Accelerating noisy variational quantum algorithms with physics-informed denoising networks
- Variational study of the magnetization plateaus in the spin-1/2 kagome Heisenberg antiferromagnet: An approach from vision transformer neural quantum states
- Compact Variational Neural Networks for Spectral Inference from a Single Nonlinear 2D Perovskite Photodetector
BibTeX
@misc{krichen2026endtoend,
title = {{End-to-End Quantum Semantic Communication with Variational Quantum Neural Networks}},
author = {Melek Krichen and Nikhitha Nunavath and Riccardo Bassoli and Soumaya Cherkaoui and Frank H. P. Fitzek},
howpublished = {arXiv},
year = {2026},
month = oct,
doi = {10.48550/arxiv.2609.25044},
eprint = {2609.25044},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2609.25044},
}
RIS
TY - UNPB
TI - End-to-End Quantum Semantic Communication with Variational Quantum Neural Networks
AU - Melek Krichen
AU - Nikhitha Nunavath
AU - Riccardo Bassoli
AU - Soumaya Cherkaoui
AU - Frank H. P. Fitzek
PY - 2026
DA - 2026/10/06
PB - arXiv
DO - 10.48550/arxiv.2609.25044
UR - https://arxiv.org/abs/2609.25044
AB - 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.
ER -