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.
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.
Traditional Knowledge Graphs (KGs), such as Neo4j, face challenges in managing high-dimensional relationships and capturing semantic nuances due to their deterministic nature. Quantum Natural Language Processing (QNLP) introduces probabilistic reasoning into the KG context. This integration leverages quantum principles, such as superposition, which allows relationships to exist in multiple states simultaneously, and entanglement, where the state of one entity dynamically influences the state of another. This quantum-based probabilistic reasoning provides a richer, more flexible representation of connections, moving beyond binary relationships to model the nuances and variability of real-world interactions. Our research demonstrates that QNLP enhances Neo4j’s ability to analyze context-rich data, improving tasks like entity extraction and knowledge inference. By modeling relationship states probabilistically, QNLP addresses limitations in traditional methods, providing nuanced insights and enabling more advanced, context-aware NLP applications.
This paper explores integrating quantum technologies with semantic communications to address the challenges of transmitting knowledge graph embeddings over traditional communication channels. As communication systems evolve to facilitate knowledge sharing and processing among natural and artificial intelligent agents, they encounter issues such as inefficiency, inaccuracy, and high complexity. To address these challenges, we propose a novel approach called quantum semantic communications, which integrates quantum technologies into the emerging framework of semantic communications. The primary goal of this protocol is to transmit semantic features efficiently, minimizing resource consumption while ensuring robustness. Our approach involves extracting semantic data from knowledge graphs using pre-trained large language model encoder and graph neural network as semantic encoders to generate compact knowledge representations transmitted over quantum communication channels. To assess the effectiveness of our protocol, we analyze performance metrics such as quantum semantic fidelity and F1 score under various noise conditions, including depolarizing, amplitude damping, and dephasing. Our simulations show that the proposed quantum semantic communication approach remains robust even in a low signal-to-noise regime.
Abstract: Quantum Natural Language Processing (QNLP) represents a pioneering approach to understanding and analyzing natural language by leveraging the principles of quantum computing. This project aims to explore the semantic analysis of autogenerated sentences using QNLP techniques. Traditional Natural Language Processing (NLP) methods have achieved significant milestones in language understanding and generation; however, they often struggle with the intricacies of context, ambiguity, and the vast computational resources required for complex tasks. QNLP offers a novel paradigm by utilizing the superposition and entanglement properties of quantum states, potentially revolutionizing how semantic meaning is extracted and processed. The primary objective of this project is to develop a QNLP framework capable of performing semantic analysis on autogenerated sentences. We will investigate the application of quantum algorithms and quantum machine learning models to decode the semantic structure and context of sentences generated by language models. This involves encoding textual data into quantum states, applying quantum gates and circuits to manipulate these states, and measuring the outcomes to interpret semantic information. By integrating quantum computing with NLP, this project seeks to achieve higher accuracy and efficiency in understanding complex language patterns. The anticipated outcomes include a comprehensive evaluation of QNLP's performance in semantic analysis, a comparison with classical NLP methods, and potential advancements in language processing tasks such as sentiment analysis, language translation, and text summarization. This project will contribute to the growing body of research at the intersection of quantum computing and artificial intelligence, paving the way for innovative solutions in the field of natural language processing.
This paper explores the potential of leveraging quantum entanglement for efficient computation and storage of semantic information, encompassing concepts and their relationships. The core claim is that utilizing quantum entanglement allows for parallel and distributed processing of semantic data, surpassing the limitations of classical computers in understanding and retrieving semantic information. The proposed mechanism involves encoding semantic information within entangled quantum states and manipulating these states through quantum gates to facilitate parallel computation and rapid exploration of complex relationships. This research represents a novel approach by integrating quantum computing with semantic understanding, offering a pathway to overcome traditional computational bottlenecks in semantic reasoning and information retrieval. The presented framework demonstrates a theoretical foundation for a new paradigm in information processing, focusing on exploiting quantum phenomena for enhanced semantic data manipulation. The key mathematical concepts and operations employed are outlined, providing a foundation for further research and development in this field. ---
Knowledge graphs (KGs) are increasingly utilized for representing and reasoning over complex data. Traditional relational reasoning methods within KGs often struggle with scalability and efficiency, particularly when dealing with large and intricate graph structures. This paper proposes a novel approach to relational reasoning in KGs by incorporating quantum-inspired techniques. Specifically, we explore the application of Grover's search algorithm to accelerate the traversal and inference processes. Our core claim is that leveraging quantum-inspired algorithms can significantly improve the performance of querying and reasoning within KGs. We present a framework for adapting Grover's algorithm to KG traversal and inference, outlining the key steps and considerations. The resulting system demonstrates the potential for enhanced efficiency compared to traditional symbolic reasoning, particularly in scenarios with high computational complexity. We provide a detailed mathematical formulation of the underlying concepts and algorithms. The primary contribution of this work lies in demonstrating a viable pathway to harnessing the power of quantum-inspired methods for relational reasoning in knowledge graphs, opening possibilities for more scalable and intelligent knowledge representation and reasoning systems.
Quantum Natural Language Processing (QNLP) is an emerging interdisciplinary field at the intersection of quantum computing, natural language understanding, and formal linguistic theory. As advances in quantum hardware and algorithms accelerate, QNLP promises new paradigms for representation learning, semantic modeling, and efficient computation. However, existing literature remains fragmented, with no unified synthesis across modeling, encoding, and evaluation dimensions.In this work, we present the first systematic and taxonomy driven survey of QNLP that holistically organizes research spanning three core dimensions: computational models, encoding paradigms, and evaluation frameworks. First, we analyze foundational approaches that map linguistic structures into quantum formalism, including categorical compositional models, variational quantum circuits, and hybrid quantum classical architectures. Second, we introduce a unified taxonomy of encoding strategies, ranging from quantum tokenization and state preparation to embedding based encodings, highlighting tradeoffs in scalability, noise resilience, and expressiveness. Third, we provide the first comparative synthesis of evaluation methodologies, benchmark datasets, and performance metrics, while identifying reproducibility and standardization gaps.We further contrast quantum inspired NLP methods with fully quantum implemented systems, offering insights into resource efficiency, hardware feasibility, and real world applicability. Finally, we outline open challenges such as integration with LLMs and unified benchmark design, and propose a research agenda for advancing QNLP as a scalable and reliable discipline.