GPR-155 Integration of Quantum Natural Language Processing (QNLP) with Neo4j LLM Knowledge Graphs for Enhanced NLP Tasks

This study investigates the integration of Quantum Natural Language Processing (QNLP) with Neo4j LLM Knowledge Graphs (KGs) to enhance natural language understanding tasks. By leveraging quantum circuit simulations, we aim to improve the probabilistic interpretation of relationships between entities. Our preliminary findings suggest that QNLP offers deeper insights compared to traditional NLP methods, particularly in modeling complex entity relationships. This approach also addresses significant limitations in Neo4j-based Large Language Model (LLM) Graph Databases, such as handling high dimensional relationships and capturing semantic nuances. The integration of QNLP into Neo4j refines relationship modeling and enhances performance in tasks like entity extraction and knowledge inference, paving the way for more advanced and context-aware NLP applications.