Incorporating Quantum-Inspired Concepts into Word Embeddings

This paper presents a novel approach to enhancing word embeddings by integrating quantum-inspired concepts such as superposition, entanglement, and measurement. Traditional word embedding models like Word2Vec, GloVe, and FastText capture semantic relationships between words but often struggle with contextual ambiguity, polysemy, and complex inter-word dependencies. Our proposed model leverages the mathematical framework of quantum mechanics to represent word meanings as dynamic, context-sensitive entities within a complex Hilbert space. We introduce superposition states to allow words to embody multiple potential meanings simultaneously and apply entanglement to model the dependencies between frequently co-occurring words. Furthermore, we use a quantum measurement process to dynamically adjust word embeddings based on context, offering a more nuanced understanding of meaning. The proposed model is evaluated on the WordSim-353 dataset, demonstrating significant improvements over classical and existing quantum-inspired approaches. Our quantum-inspired embeddings achieve a Mean Squared Error (MSE) of 0.0489 and a Pearson Correlation of 0.7766, outperforming the baselines by a substantial margin. These results highlight the potential of quantum-inspired techniques to enhance natural language processing tasks, including semantic search, text classification, and machine translation. Keywords: Quantum-Inspired NLP, Word Embeddings, Superposition, Entanglement, Contextual Adaptation, Natural Language Processing, Semantic Analysis, Quantum Measurement