Applications and research gaps in quantum natural language processing: a scientometric analysis

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.