End-to-End Quantum Semantic Communication with Variational Quantum Neural Networks

2026-08-30

AI Summary

This study addresses the challenges of scalable reasoning and inefficient information processing in quantum semantic communication by proposing an end-to-end framework that integrates quantum machine learning with semantic communication. Methodologically, variational quantum emitters are employed for feature encoding and transmission over quantum channels. A novel receiver-aware joint optimization mechanism is introduced, enabling the transmitter to adaptively mitigate channel noise while preserving task-relevant information. Furthermore, variational quantum neural networks are incorporated to facilitate end-to-end joint training. Experimental results demonstrate that the proposed framework achieves robust inference on the MNIST dataset under both ideal and noisy channel conditions, significantly outperforming baseline models.

Abstract

This paper presents a quantum semantic communication (QSemCom) framework combining quantum machine learning (QML) and semantic communication (SemCom). Classical data are compressed into low-dimensional semantic representations, encoded and processed by a variational quantum transmitter, transmitted through a quantum channel, and processed by a trainable quantum receiver for classification. The framework considers a distributed quantum communication scenario in which quantum processing units (QPUs) exchange task-relevant semantic information through quantum links. While the general setting may involve multiple quantum nodes, this work focuses on the fundamental two-node case, with transmitter and receiver QPUs connected through a noisy quantum channel. Using MNIST, the framework is evaluated under ideal, bit-flip, depolarizing, and amplitude-damping channels. A baseline model is first trained over a perfect channel and evaluated under increasing noise without retraining. Receiver-side end-to-end training is then performed at fixed depolarizing-noise levels. The perfect-channel model achieves an accuracy of $0.9556$ and an F1-score of $0.9551$. Results show channel-dependent performance degradation, while receiver training substantially restores task performance under moderate and high depolarizing noise. Moreover, task recovery does not require reconstruction of the transmitted density matrix, highlighting a distinction between physical-state recovery and semantic-feature recovery. These results demonstrate that a trainable quantum receiver can recover task-relevant semantic information from noise-distorted quantum states and maintain high classification performance.

Problem

• Quantum Semantic Communication
• Quantum Machine Learning
• Scalable Inference
• Noisy Quantum Channel
• End-to-End Learning

Innovation

• Quantum Semantic Communication
• Variational Quantum Neural Networks
• End-to-End Learning
• Quantum Machine Learning
• Noisy Quantum Channel