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Quantum Semantic Communications for Resource-Efficient Quantum Networking

Mahdi Chehimi, Christina Chaccour, Christo Kurisummoottil Thomas, Walid Saad

TL;DR

The paper tackles resource efficiency in quantum communication networks by moving beyond blind compression of quantum embeddings to semantic-aware transmission. It introduces the Quantum Semantic Communications (QSC) framework that leverages quantum machine learning and quantum semantic representations to extract and transmit only the semantically relevant information as high-dimensional qudits. Key contributions include a quantum data embedding pipeline, a quantum k-means clustering approach for semantics extraction, a semantic representation encoding to minimize quantum resources, and a formal minimality-accuracy tradeoff with fidelity bounds under a depolarizing-noise model. Simulation results demonstrate 50-75% reductions in quantum resource usage compared to semantic-agnostic schemes, while achieving higher quantum semantic fidelity and maintaining performance under realistic noise.

Abstract

Quantum communication networks (QCNs) utilize quantum mechanics for secure information transmission, but the reliance on fragile and expensive photonic quantum resources renders QCN resource optimization challenging. Unlike prior QCN works that relied on blindly compressing direct quantum embeddings of classical data, this letter proposes a novel quantum semantic communications (QSC) framework exploiting advancements in quantum machine learning and quantum semantic representations to extracts and embed only the relevant information from classical data into minimal high-dimensional quantum states that are accurately communicated over quantum channels with quantum communication and semantic fidelity measures. Simulation results indicate that, compared to semantic-agnostic QCN schemes, the proposed framework achieves approximately 50-75% reduction in quantum communication resources needed, while achieving a higher quantum semantic fidelity.

Quantum Semantic Communications for Resource-Efficient Quantum Networking

TL;DR

The paper tackles resource efficiency in quantum communication networks by moving beyond blind compression of quantum embeddings to semantic-aware transmission. It introduces the Quantum Semantic Communications (QSC) framework that leverages quantum machine learning and quantum semantic representations to extract and transmit only the semantically relevant information as high-dimensional qudits. Key contributions include a quantum data embedding pipeline, a quantum k-means clustering approach for semantics extraction, a semantic representation encoding to minimize quantum resources, and a formal minimality-accuracy tradeoff with fidelity bounds under a depolarizing-noise model. Simulation results demonstrate 50-75% reductions in quantum resource usage compared to semantic-agnostic schemes, while achieving higher quantum semantic fidelity and maintaining performance under realistic noise.

Abstract

Quantum communication networks (QCNs) utilize quantum mechanics for secure information transmission, but the reliance on fragile and expensive photonic quantum resources renders QCN resource optimization challenging. Unlike prior QCN works that relied on blindly compressing direct quantum embeddings of classical data, this letter proposes a novel quantum semantic communications (QSC) framework exploiting advancements in quantum machine learning and quantum semantic representations to extracts and embed only the relevant information from classical data into minimal high-dimensional quantum states that are accurately communicated over quantum channels with quantum communication and semantic fidelity measures. Simulation results indicate that, compared to semantic-agnostic QCN schemes, the proposed framework achieves approximately 50-75% reduction in quantum communication resources needed, while achieving a higher quantum semantic fidelity.
Paper Structure (11 sections, 1 theorem, 11 equations, 3 figures)

This paper contains 11 sections, 1 theorem, 11 equations, 3 figures.

Table of Contents

  1. Introduction
  2. System Model
  3. Quantum Data Embedding
  4. Quantum Clustering for Semantics' Extraction
  5. Problem formulation for quantum k-means clustering
  6. Solution to \ref{['eq_kmeans']}
  7. Quantum Communication of Semantic Representations
  8. QSC Problem Formulation and Performance Assessment Framework
  9. Simulation Results and Analysis
  10. Conclusion
  11. Proof of Lemma \ref{['theorem_se_rep']}

Key Result

Lemma 1

For a quantum state space $\mathcal{Y}$, and a semantic context distribution $p(c)$, the average number of quantum communication resources, $C$, required to represent the state description in the QSC framework can be bounded as: while for a semantic-agnostic QCN with amplitude encoding, the bounds are: Comparing eq_RepLength_Theorem and eq_classicalbound, the bounds for the QSC framework are low

Figures (3)

  • Figure 1: Illustrative figure showcasing the proposed QSC framework.
  • Figure 2: Comparison of communication resources for QSC and semantic agnostic networks (for which $d_2=3$).
  • Figure 3: Semantic fidelity vs quantum communication resources used, for fixed $\lvert\mathcal{X}\rvert$.

Theorems & Definitions (1)

  • Lemma 1