This research paper explores the novel intersection of quantum computing techniques and
natural language processing (NLP) algorithms, examining how quantum principles can
overcome computational bottlenecks in traditional NLP approaches. The exponential growth
in unstructured text data has pushed classical computing architectures to their limits,
particularly when processing complex language models with billions of parameters. This study
investigates quantum algorithms such as Grover's search algorithm, quantum machine learning
techniques, and quantum-inspired tensor networks as potential solutions for optimizing key
NLP tasks including sentiment analysis, machine translation, and text summarization. Through
comparative analysis of quantum and classical approaches, we demonstrate significant
theoretical speedups in computational efficiency while addressing implementation challenges
on current noisy intermediate-scale quantum (NISQ) devices. Our findings suggest that hybrid
quantum-classical architectures offer the most promising near-term approach, with quantum
advantage becoming increasingly evident for specific NLP workloads as quantum hardware
continues to mature. The research contributes to the emerging field of quantum natural
language processing (QNLP) by providing a systematic evaluation of...