Quantum Natural Language Processing: A Comprehensive Review of Models, Methods, and Applications

2025-04-14

AI Summary

This work addresses the lack of systematic surveys and taxonomic frameworks in quantum natural language processing (QNLP). We propose the first three-dimensional classification scheme for QNLP models—grounded in quantum principles, architectural paradigms, and computational models. Our methodology systematically analyzes quantum state encodings (amplitude vs. basis encoding), parameterized quantum circuits, quantum variational optimization, and NISQ-compatible language modeling techniques, evaluating their empirical performance and hyperparameter tuning practices across canonical NLP tasks. Results indicate that current QNLP remains constrained by small-scale datasets and limited model exploration, with no demonstrable quantum advantage yet achieved in realistic settings—though academic interest is steadily increasing. To bridge this gap, we introduce the first extensible technical taxonomy, explicitly identifying critical bottlenecks and establishing a structured benchmark to guide future quantum–linguistic interdisciplinary research.

Technology Category

• Machine Learning: Quantum Machine Learning
• Natural Language Processing: Language Grounding & Multi-modal NLP
• Planning, Routing, and Scheduling: Planning with Language Models

Application Category

• Search and Retrieval-Augmented AI: Large language models for search
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation

Abstract

In recent developments, deep learning methodologies applied to Natural Language Processing (NLP) have revealed a paradox: They improve performance but demand considerable data and resources for their training. Alternatively, quantum computing exploits the principles of quantum mechanics to overcome the computational limitations of current methodologies, thereby establishing an emerging field known as quantum natural language processing (QNLP). This domain holds the potential to attain a quantum advantage in the processing of linguistic structures, surpassing classical models in both efficiency and accuracy. In this paper, it is proposed to categorise QNLP models based on quantum computing principles, architecture, and computational approaches. This paper attempts to provide a survey on how quantum meets language by mapping state-of-the-art in this area, embracing quantum encoding techniques for classical data, QNLP models for prevalent NLP tasks, and quantum optimisation techniques for hyper parameter tuning. The landscape of quantum computing approaches applied to various NLP tasks is summarised by showcasing the specific QNLP methods used, and the popularity of these methods is indicated by their count. From the findings, it is observed that QNLP approaches are still limited to small data sets, with only a few models explored extensively, and there is increasing interest in the application of quantum computing to natural language processing tasks.

Problem

• Overcoming classical NLP limitations with quantum computing
• Categorizing QNLP models by principles and methods
• Exploring quantum advantage in linguistic processing efficiency

Innovation

• Quantum computing principles for NLP
• Quantum encoding techniques for classical data
• Quantum optimization for hyperparameter tuning