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

TL;DR

Quantum Natural Language Processing (QNLP) addresses the data and resource burdens of classical NLP by leveraging quantum computing to encode linguistic structure and accelerate processing. The paper surveys a taxonomy of QNLP models (categorical, probabilistic, circuit-based, kernel, language-model, and hybrid) and encoding/optimization techniques, emphasising near-term applicability on NISQ devices. Key findings show QNLP is still data-limited with a growing but uneven adoption across tasks, with sentiment analysis and text classification being the most explored, and quantum circuit models and QNNs being most prevalent. The work highlights the potential for quantum advantage in language tasks through encoding efficiency, model expressivity, and quantum-accelerated optimization, while noting hardware limitations and the need for scalable, hybrid approaches. Overall, the study maps current progress and identifies avenues to advance scalable, quantum-assisted NLP.

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.

Figures (10)

• Figure 1: QNLP Adoption by lambeq and QNN models
• Figure 2: Review methodology of the SLR stages
• Figure 3: Quantum Encoding
• Figure 4: QNLP Techniques for Sentiment Analysis
• Figure 5: Text Classification