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.
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.
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.
A study published on bioRxiv demonstrates for the first time the technique of treating protein sequences like sentences in natural language processing (NLP) and parsing them into parameterized quantum circuits using the Quantum Natural Language (QNLP) framework. It establishes a scheme that converts protein sequences into tensor networks represented by wires and boxes, and maps them onto actual quantum circuits through semantic functors. By generating protein syntax trees with a neural network-based parser and converting them into string diagrams optimized for quantum processor execution, the study proves that 'Quantum Tensor Networks (QTN)' achieve overwhelming computational accuracy in classifying vast protein sequences. [Quantum Biology Society] An innovative approach has been proposed to decode protein sequences—the most fundamental and complex codes of living organisms—by converting them into a natural language that quantum computers can comprehend. The paper titled "Application of Quantum Tensor Networks for Protein Classification", published in the preprint repository bioRxiv, introduces a groundbreaking pipeline that applies Quantum Natural Language Processing (QNLP) technology to protein sequence classification, opening a new horizon for next-generation bioinformatics. A research team from Pennsylvania State University, including Debarshi Kundu, Jian Wang, Archisman Ghosh, and Nikolay Dokholyan, led this innovative study. ■ QNLP: Reading Protein Sequences as Sentences The researchers introduced a unique perspective of treating protein sequences, which are arrays of amino acids, as if they were sentences composed in human language. To achieve this, they adopted the 'Quantum Natural Language (QNLP)' framework, which combines state-of-the-art computational natural language processing (NLP) techniques with quantum computing. Within this framework, protein sequences are parsed into syntax trees with grammatical structures through a neural network-based parser, laying the foundation for their direct conversion into parameterized quantum circuits. ■ Transformation into Tensor Networks and String Diagrams In this process, protein sequences are converted into a tensor network scheme, geometrically represented by wires and boxes. The researchers defined a sophisticated semantic function (functor) that perfectly maps this abstract network to the operational structure of actual quantum circuits. The generated syntax trees are ultimately transformed into string diagrams, a format optimized for execution on quantum processors. Consequently, the constructed Quantum Tensor Networks (QTN) demonstrated overwhelming computational accuracy in machine learning tasks that involve extracting features and functionally classifying highly diverse and complex protein sequences, even in environments with a limited number of available qubits. This demonstration is highly significant as it establishes a powerful workflow in drug discovery and biotechnology, enabling rapid and accurate analysis of massive genomic data and protein interaction structures through quantum algorithms, transcending the limits of conventional classical computers. #QuantumTensorNetworks #QNLP #ProteinClassification #QuantumMachineLearning #QuantumLanguageModel #ProteinSequenceAnalysis #QuantumBiology #Biophysics #ArtificialIntelligence #KoreanQuantumBiologySociety https://www.biorxiv.org/content/10.1101/2024.03.11.584501v1
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) 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.
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.