Abstract Quantum Natural Language Processing (QNLP) applies quantum computational models to the representation and processing of language, yet its literature remains fragmented, and no prior study treats it as an autonomous object of analysis. This study maps the field by combining scientometric analysis with a qualitative, category-based assessment. The corpus was drawn from Scopus and Web of Science using search strings that combine quantum-computing and natural-language-processing descriptors; after cleaning, deduplication, and eligibility screening, 128 articles were retained. Scientometric methods examined publication trends, influential authors, collaboration networks, sources, and keyword co-occurrence, while each article was manually classified by application area and language-processing technique. Research concentrates on text representation and classification, with a predominance of foundational methods and tools. Health and biosciences, accounting and finance, environment and energy, and text generation appear markedly underrepresented, but comparison with domain-specific reviews indicates that these scarcities are not all of one kind: the apparent scarcity of health-related work substantially reflects the database and descriptor scope of general-purpose searches rather than inactivity, whereas the scarcity of text generation is at least partly consistent with the documented limits of current quantum hardware. Distinguishing artifactual from substantive gaps requires combining quantitative mapping with qualitative categorization and has direct implications for innovation policy and research management: it supports targeted funding for quantum-native generative architectures, collaboration with domain specialists where gaps survive scrutiny, and broader cross-database indexing to avoid systematically underestimating activity in fast-moving interdisciplinary fields.
Abstract Quantum Natural Language Processing (QNLP) applies quantum computational models to the representation and processing of language, yet its literature remains fragmented, and no prior study treats it as an autonomous object of analysis. This study maps the field by combining scientometric analysis with a qualitative, category-based assessment. The corpus was drawn from Scopus and Web of Science using search strings that combine quantum-computing and natural-language-processing descriptors; after cleaning, deduplication, and eligibility screening, 128 articles were retained. Scientometric methods examined publication trends, influential authors, collaboration networks, sources, and keyword co-occurrence, while each article was manually classified by application area and language-processing technique. Research concentrates on text representation and classification, with a predominance of foundational methods and tools. Health and biosciences, accounting and finance, environment and energy, and text generation appear markedly underrepresented, but comparison with domain-specific reviews indicates that these scarcities are not all of one kind: the apparent scarcity of health-related work substantially reflects the database and descriptor scope of general-purpose searches rather than inactivity, whereas the scarcity of text generation is at least partly consistent with the documented limits of current quantum hardware. Distinguishing artifactual from substantive gaps requires combining quantitative mapping with qualitative categorization and has direct implications for innovation policy and research management: it supports targeted funding for quantum-native generative architectures, collaboration with domain specialists where gaps survive scrutiny, and broader cross-database indexing to avoid systematically underestimating activity in fast-moving interdisciplinary fields.
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.
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 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.
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.
Purpose: This study explores the improvement in the area of Quantum computing techniques and natural language algorithm, to assist researchers to earn new awareness and detect emerging areas of growth for further research in this field. Methodology: In this study, we apply bibliometric analysis by using R Studio and VOS viewer software, we got our data from the Scopus database after searching via keywords, and before the exclusion, we got 292 papers, then we start to apply the exclusion criteria such as (time limit from 1990 to 2024, Document type: Article, Publication stage: Final, Source type: Journal, and Language: English) then after we applied those criteria we reach to 273 papers. Finding: The results of this study established the development status of the field of Quantum computing techniques and natural language algorithm, we demonstrate the growth rate, and emerging thematic areas for further research, we also identified the most impactful and leading authors, topic trends, keywords, sources, publications, and the relationship between co-authors. These findings assist researchers to guide new research to concentrate on future topic trends. Originality: This study contributes to the literature by identifying the growing interest in Quantum computing techniques and natural language algorithm.
[EN] Quantum computing is widely regarded as one of the scientific fields with the greatest transformative potential. Its applications are already being explored in areas such as cybersecurity, chemistry, and machine learning. Within the domain of quantum machine learning, one emerging subfield is quantum natural language processing (QNLP), which seeks to leverage quantum algorithms to address complex language-related tasks more efficiently than classical counterparts. This thesis begins with a systematic literature review of the current landscape of quantum machine learning, focusing particularly on the design, development, and deployment of QNLP models on real quantum hardware. The SLR identifies 94 relevant articles that utilize quantum machine learning techniques and algorithms, highlighting their implementation via computational quantum circuits, or ansatzes. The main types of algorithms found include quantum adaptations of classical models such as support vector machines and k-nearest neighbors, as well as quantum neural networks, one of the most prominent applications being image classification. In addition, this work offers both quantitative and qualitative analyses, integrating a comprehensive set of experiments conducted on quantum simulators and real quantum devices. A significant contribution of this work lies in the design and evaluation of these new quantum circuit architectures, tailored to optimize expressibility, entanglement, and trainability. Empirical analysis shows that model accuracy tends to exhibit an inverse relationship with ansatz expressibility and a direct correlation with entanglement capability. In parallel, it contributes to the field by proposing novel ansatz architectures and applying circuit knitting techniques to overcome current hardware limitations. Ultimately, this thesis demonstrates the feasibility of hybrid quantum-classical approaches in QNLP and highlights their potential to be extended to support more complex and scalable real-world language applications.