Hybrid Quantum-Classical Models for Natural Language Processing

EasyChair Preprint 14859

Abstract

Natural Language Processing (NLP) has seen remarkable advancements with classical machine learning models, but scaling computational efficiency remains a challenge as tasks grow more complex. Hybrid quantum-classical models present a promising approach by leveraging quantum computing's potential for high-dimensional space exploration and pattern recognition. This paper explores the integration of quantum computing with classical architectures for NLP tasks such as language translation, sentiment analysis, and text classification. By utilizing quantum algorithms like the Variational Quantum Eigensolver (VQE) and Quantum Approximate Optimization Algorithm (QAOA), we demonstrate how quantum circuits can enhance model efficiency in handling large datasets. Preliminary results suggest that hybrid models could reduce computational costs and improve the performance of NLP systems in areas such as context comprehension and word embeddings. However, limitations in current quantum hardware and the need for scalable quantum algorithms highlight the ongoing challenges. Future directions include improving qubit stability and developing more efficient hybrid frameworks to make quantum-enhanced NLP practical on a wider scale.

Keyphrases: Hybrid Quantum-Classical, Natural Language Processing, models

@booklet{EasyChair:14859,
  author    = {Favour Olaoye and Kaledio Potter},
  title     = {Hybrid Quantum-Classical Models for Natural Language Processing},
  howpublished = {EasyChair Preprint 14859},
  year      = {EasyChair, 2024}}