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Quantum Knowledge Graph: Leveraging QNLP for Semantic Relationship Modeling
Conference proceeding   Peer reviewed

Quantum Knowledge Graph: Leveraging QNLP for Semantic Relationship Modeling

Suman Bharti, Dan Chia-Tien Lo and Hossain Shahriar
Proceedings : annual International Computer Software and Applications Conference, pp.1514-1517
Annual Computers, Software, and Applications Conference (COMPSAC), 49th (Toronto, Ontario, Canada, 07/08/2025–07/11/2025)
07/08/2025
DOI: https://doi.org/10.1109/COMPSAC65507.2025.00192
Web of Science ID: WOS:001575960000185

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Abstract

LLM Knowledge Graph Builder Neo4j Quantum Knowledge Graphs (QKG) Quantum Natural Language Processing (QNLP) parallel relationship processing Data Processing Semantics
The integration of quantum computing with knowledge graphs presents a transformative approach to intelligent information processing that enables enhanced reasoning, semantic understanding, and large-scale data inference. This study introduces a Quantum Knowledge Graph (QKG) framework that combines Neo4j's LLM Knowledge Graph Builder with Quantum Natural Language Processing (QNLP) to improve the representation, retrieval, and inference of complex knowledge structures. The proposed methodology involves extracting structured relationships from unstructured text, converting them into quantum-compatible representations using Lambeq, and executing quantum circuits via Qiskit to compute quantum embeddings. Using superposition and entanglement, the QKG framework enables parallel relationship processing, contextual entity disambiguation, and more efficient semantic association. These enhancements address the limitations of classical knowledge graphs, such as deterministic representations, scalability constraints, and inefficiencies in the capture of complex relationships. This research highlights the importance of integrating quantum computing with knowledge graphs, offering a scalable, adaptive, and semantically enriched approach to intelligent data processing.

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