Quantum natural language processing and its applications in bioinformatics: a comprehensive review of methodologies, concepts, and future directions

Quantum Natural Language Processing (QNLP) is a relatively new subfield of research that extends the application of principles of natural language processing and quantum computing that has enabled the processing of complex biological information to unprecedented levels. The present comprehensive review analyses the potential of QNLP in influencing many branches of bioinformatics such as genomic sequence analysis, protein structure prediction, and drug discovery and design. To establish a correct background of QNLP techniques, this article is going to explore the basics of quantum computing including qubits, quantum entanglement, and quantum algorithms. The next section is devoted to the application of QNLP in the extraction of material and valuable information and knowledge related to drug discovery and development, prediction and assessment of drug-target interactions. In addition, the paper also explains the application of QNLP in protein structural prediction by quantum embedding, quantum simulation, and quantum optimization for exploring the sequence-structure relationship. However, this study also acknowledges the future of QNLP in bioinformatics in the discussion of the challenges and weaknesses of quantum hardware, data representation, encoding, and the construction and enhancement of the algorithms. This looks into real-life problems solved from industry applications, benchmarking and assessment criteria, and a comparison with other traditional NLP methods. Therefore, the review enunciates the research and application perspectives, as well as the developmental and implementation blueprint for QNLP in bioinformatics. The plan is as follows: its function is to achieve the objectives of precision medicine, new protein design, multi-omics, and green chemistry.