While having enhanced NLP, deep learning and pre-trained language models requires a lot of processing power. The work showcases the potential of quantum computing by mapping linguistic data into vast, high-dimensional Hilbert spaces through entanglement and superposition. It focuses on mathematical concepts that set quantum approaches apart from classical ones, among them being the fidelity-based similarity and quantum probability. Various quantum machine learning models are considered in this article, including Quantum Neural Networks and Quantum Support Vector Machines, each discussing the computational advantages in pattern recognition. In addition, it considers retrieval techniques like Grover’s algorithm, showing how quantum similarity functions give better semantic search. Indeed, the comparison does show that quantum techniques might yield advantages regarding expressiveness and scalability, despite obstacles such as hardware noise and data encoding. Notwithstanding that quantum technology is still in its infancy, future improvements might advance language understanding.