Modern NLP models deliver strong performance but demand heavy computation and exhibit slow, brittle optimization over high-dimensional, non-convex objectives. Quantum computing offers an alternative by exploiting superposition and entanglement to explore parameter spaces more efficiently. This work develops a theoretical mapping from classical loss minimization to quantum cost expectations and evaluates quantum variational approaches (e.g., QAOA/VQE) for NLP optimization alongside classical and hybrid baselines. Methods: we adopt a comparative, simulation-based design using public datasets (IMDb, SemCor, Stanford Sentiment Treebank, WMT). Baselines include TF-IDF+Logistic Regression, LSTM, and BERT; quantum models use Qiskit QuantumKernel SVM with reduced 4-feature encodings on Aer; hybrids combine classical vectorization with quantum kernels. Results: classical models lead in Sentiment Classification (87%) and Semantic Analysis (86%), while quantum and hybrid models show resilience on ambiguity-heavy data. In Word Sense Disambiguation, the hybrid model attains 76% accuracy, outperforming classical (68%) and quantum (73%); in sentiment tasks the quantum and hybrid models reach 70% and 72%, respectively, and 66%/71% on semantic analysis. Confusion matrices indicate fewer boundary errors for hybrid configurations and suggest implicit regularization and faster convergence under low-resource settings. Conclusion: hybrid quantum–classical pipelines offer a practical route to near-term gains in ambiguity resolution and optimization efficiency, with full benefits contingent on advances in quantum hardware and native quantum embeddings/attention.