QCMI-Based Quantum Markov Blanket Discovery for Semantic Quantum Networks

2026-09-19

AI Summary

本文通过引入基于量子条件互信息的量子马尔可夫毯（QMBs）框架，解决了量子语义通信网络中资源消耗大和噪声问题，优化了量子信息隔离与传输，提高了安全性和效率。

Abstract

Quantum-enabled semantic communication networks (QESCs) leverage quantum technologies to transmit data meaning efficiently, yet face challenges from costly resources and noise. This letter introduces Quantum Markov Blankets (QMBs) to QESCs, a novel framework to isolate essential quantum information for semantic transmission. We prove QMBs' validity using quantum conditional mutual information, showing that they shield semantic content from irrelevant subsystems. An implementation strategy optimises QMB detection, reducing resource use. Simulations suggest that QMB-based QESCs cut qubit consumption by 50\%-75\% while enhancing fidelity compared with non-optimised quantum semantic schemes. Unlike classical approaches, QMBs offer inherent security by limiting an eavesdropper's access to classical data outside the blanket. We outline future directions, including real-time QMB adaptation. This work bridges quantum information theory and semantic communication, advancing resource-efficient and secure quantum networks.

Problem

• Quantum-enabled semantic communication networks
• resource costs
• noise
• Quantum Markov Blankets

Innovation

• Quantum Markov Blankets
• quantum conditional mutual information
• resource optimization
• semantic communication
• security