OPTIMIZATION OF ALGORITHM APPLIED FOR NATURAL LANGUAGE PROCESSING (NLP) TASK USING QANTUM COMPUTING TECHNIQUES

Abstract

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.