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.
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.
By using the quantum mechanics phenomenon, quantum computers provide a new dimension of computational power that drastically accelerates solving complex and resource-intensive problems. One of the most evolving but, due to the computation power of standard computers, reasonably limited application domain is natural language processing (NLP). NLP seeks to give interactive systems the ability to understand and manipulate human language. Making systems understand and manipulate human languages requires large amounts of data and computational power during learning as well as during the execution of NLP. For handling these amounts of data like text and audio recordings and the complexity of classical NLP algorithms, quantum computation has emerged as a promising solution. This work gives an extensive overview of this new field, known as quantum natural language processing (QNLP). Introducing the basics of quantum computing, we discuss its use in NLP by explaining the different proposed embedding models, quantum algorithms, and other methods of QNLP. As QNLP is still in its infancy, this comprehensive overview is the foundation that points to the upcoming research direction.
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.
QNLP is a quite new and emerging field of inquiry that aims to utilize the principles of quantum computing to achieve NLP tasks. QNLP aims to enhance the accuracy of natural language processing by utilizing the quantum properties of matter known as superposition, interference, and, most importantly, entanglement. This book chapter introduces the basics of QNLP, including a brief overview of concepts used in quantum computing and techniques of NLP. We have explored the potential benefits of QNLP, such as faster and more accurate processing of natural language data. We also examine the challenges and limitations of QNLP, such as the need for quantum hardware and the integration with classical NLP techniques. In addition, this chapter covers recent advances in QNLP, including quantum algorithms for language modeling, machine translation, and sentiment analysis. We also discuss the development of hybrid quantum-classical algorithms and the potential applications of QNLP in industry and academia. Overall, this chapter provides a comprehensive overview of QNLP and its potential to revolutionize natural language processing.
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.