In the past few years, quantum computing and natural language processing (NLP) have converged with encouraging results into a new discipline called quantum natural language processing (QNLP). It is an interdisciplinary branch of science that seeks to apply quantum mechanics to aspects of language processing. It includes theoretical investigations as well as practical implementations on classical or quantum hardware. In addition, this chapter gives a summary of QNLP by classifying it in terms of its theoretical or hardware implementation, NLP tasks targeted, and evaluation resources employed. Furthermore, it analyzes how QNLP improves on existing methods in terms of performance and methodology, while considering its contribution to future NLP tasks that rely on currently practiced deep learning approaches. This review concludes by examining the key success factors in extant QNLP research, along with gaps and future directions.
In the past few years, quantum computing and natural language processing (NLP) have converged with encouraging results into a new discipline called quantum natural language processing (QNLP). It is an interdisciplinary branch of science that seeks to apply quantum mechanics to aspects of language processing. It includes theoretical investigations as well as practical implementations on classical or quantum hardware. In addition, this chapter gives a summary of QNLP by classifying it in terms of its theoretical or hardware implementation, NLP tasks targeted, and evaluation resources employed. Furthermore, it analyzes how QNLP improves on existing methods in terms of performance and methodology, while considering its contribution to future NLP tasks that rely on currently practiced deep learning approaches. This review concludes by examining the key success factors in extant QNLP research, along with gaps and future directions.
Quantum natural language processing (QNLP) refers to the many approaches developed within the discipline as a result of the convergence of natural language processing (NLP) and quantum computing in recent years. This multidisciplinary field involves applying quantum mechanical insights to fundamental aspects of language processing across a wide range of NLP activities. Existing methods span a broad spectrum from theoretical demonstrations of the quantum advantage to actual implementations of algorithms using quantum hardware. This work aims to do just that by bringing together, in one place, the many different methods that have been applied to the problem. Theoretical studies and practices that have been implemented on classical or quantum hardware are separated here. The categorization also takes into account the goal of each strategy. Syntax-semantic representation is an example of a general-purpose task. In this chapter, the benefits of QNLP in terms of efficiency and methodology are dissected. In addition, it explores how QNLP techniques might be utilized in place of stateof- the-art deep learning-based methodologies.
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.
The meeting between Natural Language Processing (NLP) and Quantum Computing has been very successful in recent years, leading to the development of several approaches of the so-called Quantum Natural Language Processing (QNLP). This is a hybrid field in which the potential of quantum mechanics is exploited and applied to critical aspects of language processing, involving different NLP tasks. Approaches developed so far span from those that demonstrate the quantum advantage only at the theoretical level to the ones implementing algorithms on quantum hardware. This paper aims to list the approaches developed so far, categorizing them by type, i.e., theoretical work and those implemented on classical or quantum hardware; by task, i.e., general purpose such as syntax-semantic representation or specific NLP tasks, like sentiment analysis or question answering; and by the resource used in the evaluation phase, i.e., whether a benchmark dataset or a custom one has been used. The advantages offered by QNLP are discussed, both in terms of performance and methodology, and some considerations about the possible usage QNLP approaches in the place of state-of-the-art deep learning-based ones are given.
Quantum Natural Language Processing (QNLP) is an emerging interdisciplinary field at the intersection of quantum computing, natural language understanding, and formal linguistic theory. As advances in quantum hardware and algorithms accelerate, QNLP promises new paradigms for representation learning, semantic modeling, and efficient computation. However, existing literature remains fragmented, with no unified synthesis across modeling, encoding, and evaluation dimensions.In this work, we present the first systematic and taxonomy driven survey of QNLP that holistically organizes research spanning three core dimensions: computational models, encoding paradigms, and evaluation frameworks. First, we analyze foundational approaches that map linguistic structures into quantum formalism, including categorical compositional models, variational quantum circuits, and hybrid quantum classical architectures. Second, we introduce a unified taxonomy of encoding strategies, ranging from quantum tokenization and state preparation to embedding based encodings, highlighting tradeoffs in scalability, noise resilience, and expressiveness. Third, we provide the first comparative synthesis of evaluation methodologies, benchmark datasets, and performance metrics, while identifying reproducibility and standardization gaps.We further contrast quantum inspired NLP methods with fully quantum implemented systems, offering insights into resource efficiency, hardware feasibility, and real world applicability. Finally, we outline open challenges such as integration with LLMs and unified benchmark design, and propose a research agenda for advancing QNLP as a scalable and reliable discipline.
Quantum Natural Language Processing (QNLP) is an emerging interdisciplinary field that connects quantum computing and natural language processing (NLP), with the goal of improving language models through quantum algorithms. Although the field has gained increasing attention, research in QNLP remains fragmented, with limited studies mapping its evolution or practical implementations. This article presents a comprehensive scientometric analysis of QNLP research, revealing its conceptual foundations, research dynamics, and emerging directions. Using bibliographic data from the Scopus (2014-2024) and Web of Science (2014-2024) databases and analytical tools such as VOSviewer and Biblioshiny, we examined 116 publications following the PRISMA guidelines. The analysis covers publication growth, author productivity, leading journals, influential countries, and keyword trends. Network visualizations highlight the intellectual structure of the field, co-authorship patterns, and thematic clusters. The results indicate a sharp increase in QNLP studies since 2021, driven by advances in quantum hardware, frameworks such aslambeq, and growing interdisciplinary collaborations. However, during the literature mapping, we identified a significant gap regarding the applications of QNLP in various specific domains such as healthcare field. This study offers a quantitative mapping of the QNLP landscape, providing a structured overview of its development and outlining promising avenues for future exploration.
Abstract: Quantum Natural Language Processing is the implementation of NLP algorithms on quantum hardware or alternatively on hybrid quantum-classical hardware. NLP has been a heavily researched and implemented topic of the past few decades and the most recent developments using new techniques and the power of deep learning have made huge strides in the field. But for all this new development, there is a looming possibility of greater achievements in the form of the rising field of quantum computing which is yet to see its potential come to fruition. A gaping hole in the implementation process of NLP systems is the computing power required to train deep learning and Natural Language Processing models which makes the development of such models time consuming and power hungry. The huge leap in parallel computing power that quantum computers provide gives us immense opportunities to accelerate the training of deep and complex models. Such techniques will help organizations with access to quantum hardware to be able to use quantum circuits to either train a complete model or use a classical system like the norm but outsource all of the most computationally heavy part of the process to quantum hardware which will provide exponential speed up to the development of conversational AI models. Keywords: Quantum computing, Natural Language Processing, Quantum Machine Learning, Quantum Natural Language Processing, Noisy Intermediate-Scale Quantum systems, Lambeq, hybrid classical-quantum systems, DisCoCat