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Journal articles on the topic 'Quantum natural language processing'

Author: Grafiati

Published: 7 June 2025

Last updated: 2 August 2025

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1

Guarasci, Raffaele, Giuseppe De Pietro, and Massimo Esposito. "Quantum Natural Language Processing: Challenges and Opportunities." Applied Sciences 12, no. 11 (2022): 5651. http://dx.doi.org/10.3390/app12115651.

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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 approac

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Rai, Anshuman. "A Review Article on Quantum Natural Language Processing." International Journal for Research in Applied Science and Engineering Technology 10, no. 1 (2022): 1588–94. http://dx.doi.org/10.22214/ijraset.2022.40103.

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Abstract: Quantum Natural Language Processing is the implementation of NLP algorithms on quantum hardware or alternatively on hybrid quantum-classical hardware. NLP has been a heavily researched and implemented topic of the past few decades and the most recent developments using new techniques and the power of deep learning have made huge strides in the field. But for all this new development, there is a looming possibility of greater achievements in the form of the rising field of quantum computing which is yet to see its potential come to fruition. A gaping hole in the implementation process

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Meichanetzidis, Konstantinos, Stefano Gogioso, Giovanni de Felice, Nicolò Chiappori, Alexis Toumi, and Bob Coecke. "Quantum Natural Language Processing on Near-Term Quantum Computers." Electronic Proceedings in Theoretical Computer Science 340 (September 6, 2021): 213–29. http://dx.doi.org/10.4204/eptcs.340.11.

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Zeng, William, and Bob Coecke. "Quantum Algorithms for Compositional Natural Language Processing." Electronic Proceedings in Theoretical Computer Science 221 (August 2, 2016): 67–75. http://dx.doi.org/10.4204/eptcs.221.8.

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Kavya, J., K. Krishnaveni, K. Namratha, D. Lakshmi Gowri, and M. Madhavi. "Semantic Analysis of Auto-Generated Sentences Using Quantum Natural Language Processing." International Journal for Research in Applied Science and Engineering Technology 12, no. 11 (2024): 1469–72. http://dx.doi.org/10.22214/ijraset.2024.65380.

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Abstract: Quantum Natural Language Processing (QNLP) represents a pioneering approach to understanding and analyzing natural language by leveraging the principles of quantum computing. This project aims to explore the semantic analysis of autogenerated sentences using QNLP techniques. Traditional Natural Language Processing (NLP) methods have achieved significant milestones in language understanding and generation; however, they often struggle with the intricacies of context, ambiguity, and the vast computational resources required for complex tasks. QNLP offers a novel paradigm by utilizing t

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Wan, Meiyan. "Quantum mechanics and statistical physics: Novel frameworks for enhancing natural language processing." Applied and Computational Engineering 102, no. 1 (2024): 1–6. http://dx.doi.org/10.54254/2755-2721/102/20240912.

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Abstract. This article explores the pioneering application of principles from quantum mechanics and statistical mechanics to the field of natural language processing (NLP). By drawing analogies between physical phenomena such as quantum entanglement, phase transitions, and statistical ensembles, and linguistic concepts like semantic relationships, language use dynamics, and lexical diversity, we offer a novel perspective on language analysis and processing. Quantum linguistic models, leveraging the intricacies of entanglement and quantum probability, provide a framework for understanding compl

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O’Riordan, Lee J., Myles Doyle, Fabio Baruffa, and Venkatesh Kannan. "A hybrid classical-quantum workflow for natural language processing." Machine Learning: Science and Technology 2, no. 1 (2020): 015011. http://dx.doi.org/10.1088/2632-2153/abbd2e.

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Yao, Ben, Prayag Tiwari, and Qiuchi Li. "Self-supervised pre-trained neural network for quantum natural language processing." Neural Networks 184 (April 2025): 107004. https://doi.org/10.1016/j.neunet.2024.107004.

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Tangpanitanon, Jirawat, Chanatip Mangkang, Pradeep Bhadola, Yuichiro Minato, Dimitris G. Angelakis, and Thiparat Chotibut. "Explainable natural language processing with matrix product states." New Journal of Physics 24, no. 5 (2022): 053032. http://dx.doi.org/10.1088/1367-2630/ac6232.

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Abstract Despite empirical successes of recurrent neural networks (RNNs) in natural language processing (NLP), theoretical understanding of RNNs is still limited due to intrinsically complex non-linear computations. We systematically analyze RNNs’ behaviors in a ubiquitous NLP task, the sentiment analysis of movie reviews, via the mapping between a class of RNNs called recurrent arithmetic circuits (RACs) and a matrix product state. Using the von-Neumann entanglement entropy (EE) as a proxy for information propagation, we show that single-layer RACs possess a maximum information propagation ca

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Skipper, Katherine. "Ask me anything: Muhammad Hamza Waseem." Physics World 38, no. 5 (2025): 45ii. https://doi.org/10.1088/2058-7058/38/05/31.

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Muhammad Hamza Waseem is a research scientist at Quantinuum, where he works on quantum natural language processing as well as quantum physics education and outreach. His other research interests include quantum foundations, applied category theory and mathematical linguistics.

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Lorenz, Robin, Anna Pearson, Konstantinos Meichanetzidis, Dimitri Kartsaklis, and Bob Coecke. "QNLP in Practice: Running Compositional Models of Meaning on a Quantum Computer." Journal of Artificial Intelligence Research 76 (April 30, 2023): 1305–42. http://dx.doi.org/10.1613/jair.1.14329.

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Quantum Natural Language Processing (QNLP) deals with the design and implementation of NLP models intended to be run on quantum hardware. In this paper, we present results on the first NLP experiments conducted on Noisy Intermediate-Scale Quantum (NISQ) computers for datasets of size greater than 100 sentences. Exploiting the formal similarity of the compositional model of meaning by Coecke, Sadrzadeh, and Clark (2010) with quantum theory, we create representations for sentences that have a natural mapping to quantum circuits. We use these representations to implement and successfully train NL

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Lai, Wei, Jinjing Shi, and Yan Chang. "Quantum-Inspired Fully Complex-Valued Neutral Network for Sentiment Analysis." Axioms 12, no. 3 (2023): 308. http://dx.doi.org/10.3390/axioms12030308.

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Most of the existing quantum-inspired models are based on amplitude-phase embedding to model natural language, which maps words into Hilbert space. In quantum-computing theory, the vectors corresponding to quantum states are all complex values, so there is a gap between these two areas. Presently, complex-valued neural networks have been studied, but their practical applications are few, let alone in the downstream tasks of natural language processing such as sentiment analysis and language modeling. In fact, the complex-valued neural network can use the imaginary part information to embed hid

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P.Ramya, P., R. R.Anitha, J. Rajalakshmi, and R. R.Dineshkumar. "Integrating Quantum Computing and NLP for Advanced Cyber Threat Detection." Journal of Cybersecurity and Information Management 14, no. 2 (2024): 186–97. http://dx.doi.org/10.54216/jcim.140213.

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The exponential growth of digital data and the increasing sophistication of cyber threats demand more advanced methods for threat analysis. This paper explores the integration of quantum computing and natural language processing (NLP) to enhance cyber threat analysis. Traditional computing methods struggle to keep up with the scale and complexity of modern cyber threats, but quantum computing offers a promising avenue for accelerated data processing, while NLP provides sophisticated tools for interpreting and understanding human language, crucial for analysing threat intelligence. Our proposed

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Cha, Peter, Paul Ginsparg, Felix Wu, Juan Carrasquilla, Peter L. McMahon, and Eun-Ah Kim. "Attention-based quantum tomography." Machine Learning: Science and Technology 3, no. 1 (2021): 01LT01. http://dx.doi.org/10.1088/2632-2153/ac362b.

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Abstract With rapid progress across platforms for quantum systems, the problem of many-body quantum state reconstruction for noisy quantum states becomes an important challenge. There has been a growing interest in approaching the problem of quantum state reconstruction using generative neural network models. Here we propose the ‘attention-based quantum tomography’ (AQT), a quantum state reconstruction using an attention mechanism-based generative network that learns the mixed state density matrix of a noisy quantum state. AQT is based on the model proposed in ‘Attention is all you need’ by Va

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Valverde, Raul, and Chet Swanson. "The Development of a Quantum-based Ontology for the Description of the Reality Experienced in NDEs by Using Computerized NLP Analytics." NeuroQuantology 20, no. 2 (2022): 10–22. http://dx.doi.org/10.14704/nq.2022.20.2.nq22019.

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According to the survival hypothesis, a person's personality and consciousness survive the death of the physical body. Ontology is a well-established theoretical branch of philosophy concerned with representations of reality. This research proposes the use of computer natural language processing (NLP), databases, structured query language and near-death experiences (NDEs) narratives to develop a quantum ontology based on the quantum hologram theory of physics and consciousness. This research proposes the use of a quantum ontology to represent the incomprehensible aspects of near-death experien

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Zhang, Wei. "Applications of Deep Learning in Natural Language Processing: A Case Study on Machine Translation." Journal of Computer, Signal, and System Research 2, no. 1 (2025): 80–90. https://doi.org/10.71222/acs2j404.

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This paper explores the application of deep learning techniques in the field of Natural Language Processing (NLP), with a particular focus on machine translation. We trace the evolution of machine translation systems, from rule-based and statistical methods to the state-of-the-art neural approaches, highlighting the transformative role of deep learning models such as Recurrent Neural Networks (RNNs), Long Short-Term Memory (LSTM) networks, and the Transformer architecture. Through case studies of leading platforms like Google Translate and DeepL Translator, we demonstrate the practical impact

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Zhao, Ruoling. "The Advantage and Disadvantage of Development of Quantum Computing in Machine Learning." Applied and Computational Engineering 146, no. 1 (2025): 24–29. https://doi.org/10.54254/2755-2721/2025.21579.

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Quantum computing is an emergent technology with great promise to reshape machine learning. It enables computations intractable by any classical system. This paper discusses the basic concepts of quantum computing, its advantages, and their limitations regarding ML. It can be concluded that quantum computers leverage phenomena in quantum mechanics, such as superposition, entanglement, and interference execute those computations that are beyond the capability of classical systems. A few of the most important fancies in QSVM, QPCA, and QNN perform such tasks as computing kernel matrices, high-di

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Eisinger, Jurek, Ward Gauderis, Lin de Huybrecht, and Geraint A. Wiggins. "Classical Data in Quantum Machine Learning Algorithms: Amplitude Encoding and the Relation Between Entropy and Linguistic Ambiguity." Entropy 27, no. 4 (2025): 433. https://doi.org/10.3390/e27040433.

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The Categorical Compositional Distributional (DisCoCat) model has been proven to be very successful in modelling sentence meaning as the interaction of word meanings. Words are modelled as quantum states, interacting guided by grammar. This model of language has been extended to density matrices to account for ambiguity in language. Density matrices describe probability distributions over quantum states, and in this work we relate the mixedness of density matrices to ambiguity in the sentences they represent. The von Neumann entropy and the fidelity are used as measures of this mixedness. Via

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C V, Maheshwaran, and Amirdavarshni V. "AI-Driven Approaches to Enhance Cybersecurity in Financial Transactions." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 008 (2024): 1–13. http://dx.doi.org/10.55041/ijsrem37015.

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A surge in digital monetary transactions has resulted in a rise in cyber threats on such platforms. Conventional security measures are slowly eroding and are, therefore, failing to a great extent in curbing these emerging risks. Artificial Intelligence (AI) holds out much promise toward robust cybersecurity through mechanisms with machine learning and anomaly detection techniques, especially natural language processing. This paper tries to explore technical insight into the AI-based framework, approaches, applications, benefits, issues, ethical concerns, and the way forward for the security of

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Jin, Jin, Qian Zhang, Jia He, and Hongnian Yu. "Quantum Dynamic Optimization Algorithm for Neural Architecture Search on Image Classification." Electronics 11, no. 23 (2022): 3969. http://dx.doi.org/10.3390/electronics11233969.

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Deep neural networks have proven to be effective in solving computer vision and natural language processing problems. To fully leverage its power, manually designed network templates, i.e., Residual Networks, are introduced to deal with various vision and natural language tasks. These hand-crafted neural networks rely on a large number of parameters, which are both data-dependent and laborious. On the other hand, architectures suitable for specific tasks have also grown exponentially with their size and topology, which prohibits brute force search. To address these challenges, this paper propo

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Nida, Bhanu Raju. "From Classical to Quantum: The Future of Advanced Analytics with Quantum Computing." International Journal of Multidisciplinary Research and Growth Evaluation. 6, no. 2 (2025): 587–93. https://doi.org/10.54660/.ijmrge.2025.6.2.587-593.

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The rapid growth of complex analytics has moved from classical statistical methods to the AI-based systems using large data sets for predictive and prescriptive decision making. Although the traditional computing systems have greatly improved decision-making abilities, they have their limitations in the complex and large-scale data collection and real time processing and analysis. Quantum computing is a major step forward in the enhancement of computational capability with the help of principles such as superposition and entanglement to perform several calculations at a time. This paper explor

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Ogur, Besir, and Ihsan Yılmaz. "The effect of superposition and entanglement on hybrid quantum machine learning for weather forecasting." Quantum Information & Computation 23, no. 3&4 (2023): 181–94. http://dx.doi.org/10.26421/qic23.3-4-1.

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Recently, proposed algorithms for quantum computing and generated quantum computer technologies continue to evolve. On the other hand, machine learning has become an essential method for solving many problems such as computer vision, natural language processing, prediction and classification. Quantum machine learning is a new field developed by combining the advantages of these two primary methods. As a hybrid approach to quantum and classical computing, variational quantum circuits are a form of machine learning that allows predicting an output value against input variables. In this study, th

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Yu, Yan, Dong Qiu, and Ruiteng Yan. "A Quantum Language-Inspired Tree Structural Text Representation for Semantic Analysis." Mathematics 10, no. 6 (2022): 914. http://dx.doi.org/10.3390/math10060914.

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Text representation is an important topic in the field of natural language processing, which can effectively transfer knowledge to downstream tasks. To extract effective semantic information from text with unsupervised methods, this paper proposes a quantum language-inspired tree structural text representation model to study the correlations between words with variable distance for semantic analysis. Combining the different semantic contributions of associated words in different syntax trees, a syntax tree-based attention mechanism is established to highlight the semantic contributions of non-

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Aldalbahi, Majed Naji Khalaf. "Emerging Technologies in Health Informatics: A Comprehensive Review and Future Directions." Journal of Medical Science and clinical Research 13, no. 04 (2025): 34–37. https://doi.org/10.18535/jmscr/v13i04.05.

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The rapid evolution of health informatics is transforming healthcare delivery through cutting-edge technologies. This paper provides a systematic review of emerging technologies in health informatics, including artificial intelligence (AI), blockchain, natural language processing (NLP), wearable devices, and quantum computing. We analyze their applications, benefits, challenges, and future trends through case studies, comparative analyses, and empirical data. Additionally, we propose a framework for successful implementation and discuss ethical, regulatory, and interoperability considerations.

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Sanath Chilakala. "Next-Gen Transformation of Insurance Document Management." International Journal of Scientific Research in Computer Science, Engineering and Information Technology 11, no. 1 (2025): 2644–51. https://doi.org/10.32628/cseit251112277.

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The integration of Artificial Intelligence in the insurance industry's document and letter generation processes marks a transformative advancement in operational efficiency. This technical article examines the comprehensive implementation of AI technologies across various aspects of insurance documentation, including policy generation, claims processing, and customer correspondence. The article analyzes the architectural framework, focusing on Natural Language Processing and Machine Learning components that form the backbone of modern insurance systems. The article demonstrates how AI-driven s

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Bessmertny, I. A., A. V. Vasiljev, Yu A. Koroleva, A. V. Platonov, and E. A. Poleschuk. "Quantum formalism methods in information retrieval and processing of texts in natural languages." Izvestiâ vysših učebnyh zavedenij. Priborostroenie 62, no. 8 (2019): 702–9. http://dx.doi.org/10.17586/0021-3454-2019-62-8-702-709.

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Fan, Zipeng, Jing Zhang, Peng Zhang, Qianxi Lin, and Hui Gao. "Quantum-Inspired Neural Network with Runge-Kutta Method." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 16 (2024): 17977–84. http://dx.doi.org/10.1609/aaai.v38i16.29753.

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In recent years, researchers have developed novel Quantum-Inspired Neural Network (QINN) frameworks for the Natural Language Processing (NLP) tasks, inspired by the theoretical investigations of quantum cognition. However, we have found that the training efficiency of QINNs is significantly lower than that of classical networks. We analyze the unitary transformation modules of existing QINNs based on the time displacement symmetry of quantum mechanics and discover that they are resembling a mathematical form similar to the first-order Euler method. The high truncation error associated with Eul

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Xu, Wei, Han Chen, Ruifeng He, Xinlong Song, Lan Ma, and Jingyao Song. "P‐205: Exploring Potential of Language Models in OLED Materials Discovery." SID Symposium Digest of Technical Papers 55, no. 1 (2024): 2163–66. http://dx.doi.org/10.1002/sdtp.18036.

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Language Models (LMs) have recently achieved remarkable success in natural language processing and other Artificial Intelligence (AI) applications. In this work, we adopt a language‐like representation of organic molecules and utilize LMs to address two typical tasks in the discovery of Organic Light‐Emitting Diode (OLED) materials: property prediction and structure generation. In the prediction task, the LM serves as a surrogate model of the quantum chemistry simulator for electronic properties prediction. In the generation task, the LM acts as a conditional generator for generating novel mol

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Thatipelly, Rasaghna. "Artificial Intelligence and Deep Learning: Trends and Applications." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 03 (2025): 1–9. https://doi.org/10.55041/ijsrem43336.

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Different businesses have experienced improvements via Artificial Intelligence (AI) together with deep learning algorithms thanks to machines that analyze complex data and detect recurring patterns for conducting automated calculations. Deep learning, which belongs to machine learning, delivers breakthroughs in computational processes for both natural language processing and computer vision and speech recognition. Research about modern AI patterns in academia looks into contemporary computing methods such as transformer models and generative adversarial networks together with AI system integra

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Zhang, Peng, Wenjie Hui, Benyou Wang, et al. "Complex-valued Neural Network-based Quantum Language Models." ACM Transactions on Information Systems 40, no. 4 (2022): 1–31. http://dx.doi.org/10.1145/3505138.

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Language modeling is essential in Natural Language Processing and Information Retrieval related tasks. After the statistical language models, Quantum Language Model (QLM) has been proposed to unify both single words and compound terms in the same probability space without extending term space exponentially. Although QLM achieved good performance in ad hoc retrieval, it still has two major limitations: (1) QLM cannot make use of supervised information, mainly due to the iterative and non-differentiable estimation of the density matrix, which represents both queries and documents in QLM. (2) QLM

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Widdows, Dominic, Kirsty Kitto, and Trevor Cohen. "Quantum Mathematics in Artificial Intelligence." Journal of Artificial Intelligence Research 72 (December 14, 2021): 1307–41. http://dx.doi.org/10.1613/jair.1.12702.

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In the decade since 2010, successes in artificial intelligence have been at the forefront of computer science and technology, and vector space models have solidified a position at the forefront of artificial intelligence. At the same time, quantum computers have become much more powerful, and announcements of major advances are frequently in the news. The mathematical techniques underlying both these areas have more in common than is sometimes realized. Vector spaces took a position at the axiomatic heart of quantum mechanics in the 1930s, and this adoption was a key motivation for the d

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Temitope Oluwatosin Fatunmbi. "Quantum computing and Artificial Intelligence: Toward a new computational paradigm." World Journal of Advanced Research and Reviews 27, no. 1 (2025): 687–95. https://doi.org/10.30574/wjarr.2025.27.1.2498.

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This paper explores the convergence of quantum computing and artificial intelligence (AI), examining how their integration may redefine computational paradigms. Quantum computing, with its unique properties of superposition and entanglement, has the potential to exponentially accelerate AI processes, particularly in optimization, machine learning, and data analysis. We investigate quantum algorithms, such as the quantum Fourier transform and Grover’s algorithm, highlighting their application to AI models and machine learning tasks that require vast computational resources. The paper further de

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Wael, Ayham, and Amer Madi. "Accelerating Artificial Intelligence: The Role of GPUs in Deep Learning and Computational Advancements." East Journal of Engineering 1, no. 1 (2025): 31–46. https://doi.org/10.63496/eje.vol1.iss1.34.

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The advancement of Artificial Intelligence (AI) has been largely driven by improvements in hardware, particularly Graphical Processing Units (GPUs). Originally intended for graphics rendering, GPUs have become essential for AI due to their capacity for massive parallel computations. This paper explores the architectural evolution of GPUs, their applications in AI, and their impact on deep learning, natural language processing, and real-time AI systems. Additionally, challenges such as power consumption, scalability, and cost are examined, alongside emerging solutions including AI-specific acce

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Banadeshwar, Hiremath, and Y. Kenchakkanavar Anand. "A Historical Perspective on Artificial Intelligence: Development, Challenges and Future Directions." Journal of Advances in Library and Information Science 14, no. 1 (2025): 68–75. https://doi.org/10.5281/zenodo.14877653.

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<em>This study explores the evolution of Artificial Intelligence (AI) from its mid-twentieth-century theoretical roots to modern advances in machine learning, deep learning, and natural language processing. It highlights milestones like early symbolic AI, expert systems, neural networks, and generative AI models. The study examines major constraints like computation limits, ethics, bias, and regulations. It also highlights growing patterns like explainable AI, human-centred AI, and merging AI with quantum computers. The study also discusses emerging trends and potential developments in AI rese

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Cherrat, El Amine, Iordanis Kerenidis, Natansh Mathur, Jonas Landman, Martin Strahm, and Yun Yvonna Li. "Quantum Vision Transformers." Quantum 8 (February 22, 2024): 1265. http://dx.doi.org/10.22331/q-2024-02-22-1265.

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In this work, quantum transformers are designed and analysed in detail by extending the state-of-the-art classical transformer neural network architectures known to be very performant in natural language processing and image analysis. Building upon the previous work, which uses parametrised quantum circuits for data loading and orthogonal neural layers, we introduce three types of quantum transformers for training and inference, including a quantum transformer based on compound matrices, which guarantees a theoretical advantage of the quantum attention mechanism compared to their classical cou

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Ahmadi, Ali. "Quantum Computing and AI: The Synergy of Two Revolutionary Technologies." Asian Journal of Electrical Sciences 12, no. 2 (2023): 15–27. http://dx.doi.org/10.51983/ajes-2023.12.2.4118.

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An important turning point in the history of technology and computation is the confluence of Quantum Computing and Artificial Intelligence (AI). Redefining the limits of what is possible, quantum computing delivers previously unheard-of computational capabilities by utilizing the special qualities of quantum physics. AI, on the other hand, has made remarkable strides in simulating human intelligence, particularly through deep learning and natural language processing. This article explores the profound synergy arising from the intersection of Quantum Computing and AI. It explores the advantages

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Venugopal, Vineeth, Scott R. Broderick, and Krishna Rajan. "A picture is worth a thousand words: applying natural language processing tools for creating a quantum materials database map." MRS Communications 9, no. 4 (2019): 1134–41. http://dx.doi.org/10.1557/mrc.2019.136.

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Gupta, Dr Vikram. "Recent Advancements in Computer Science: A Comprehensive Review of Emerging Technologies and Innovations." International Journal for Research Publication and Seminars 14, no. 1 (2023): 329–34. http://dx.doi.org/10.36676/jrps.2023-v14i1-42.

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Recent advances in computer science have led to many new technologies and inventions that continue to affect our environment. This extensive study highlights some of the most significant computer science discoveries and their possible applications. This assessment begins with AI, which has driven many recent achievements. Cutting-edge machine learning methods, deep neural networks, reinforcement learning, and explainable AI are discussed. AI and adjacent sciences like natural language processing (NLP) and computer vision have led to innovative applications in healthcare, finance, autonomous ca

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Tyurkan (Belichenko), Elena. "Holistic Linguistics: Anthropocentric Foundations and the Functional-Cognitive Paradigm." Prague Journal of English Studies 4, no. 1 (2015): 125–56. http://dx.doi.org/10.1515/pjes-2015-0008.

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AbstractThis paper dwells on some aspects of language, grammar in particular, through the prism of the functional-cognitive approach. It covers such issues as language and mind, the embodiment of language, the peculiarities of language acquisition, and the metaphoric nature of the human mind. The functional-cognitive approach is regarded as part of a holistic anthropocentric paradigm where language is conceived of as a natural biological phenomenon connected with the adaptive functions of a human being as a holistic living organism. A new paradigm gives rise to new epistemologies and generates

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A. Sankaran and K. Sathiyamurthy. "Quantum LLM Model for Entity and Semantic Relation Extraction in Drug Interactions." Advances in Artificial Intelligence and Machine Learning 05, no. 01 (2025): 3495–518. https://doi.org/10.54364/aaiml.2025.51200.

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In modern natural language processing, it is still difficult to extract entity and semantic links from biomedical literature, such as drug-drug, drug-gene, drug-test, drug-disease, drug-herb, drug-food and drug-lab range interactions. In this work XLNet, a large language model based on transformers, is finetuned with Bayesian network that have been improved by the Quantum Approximate Optimization Algorithm (QAOA) by using directed acyclic graphs (DAGs) and Conditional Probability Tables (CPTs) to model complicated biomedical interactions. This work combines the ability of XLNet to capture two-

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Aerts, Diederik, and Lester Beltran. "Are Words the Quanta of Human Language? Extending the Domain of Quantum Cognition." Entropy 24, no. 1 (2021): 6. http://dx.doi.org/10.3390/e24010006.

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In previous research, we showed that ‘texts that tell a story’ exhibit a statistical structure that is not Maxwell–Boltzmann but Bose–Einstein. Our explanation is that this is due to the presence of ‘indistinguishability’ in human language as a result of the same words in different parts of the story being indistinguishable from one another, in much the same way that ’indistinguishability’ occurs in quantum mechanics, also there leading to the presence of Bose–Einstein rather than Maxwell–Boltzmann as a statistical structure. In the current article, we set out to provide an explanation for thi

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Pillai, Preeta. "Revolutionizing Financial Services: The Impact of AI-Driven Data Pipelines." European Journal of Computer Science and Information Technology 13, no. 18 (2025): 91–100. https://doi.org/10.37745/ejcsit.2013/vol13n1891100.

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The integration of artificial intelligence in financial data pipeline management has revolutionized the operational landscape of financial services. This transformation encompasses enhanced processing capabilities, automated validation systems, and sophisticated predictive analytics that have redefined traditional banking operations. The advancement in ETL processes has led to substantial improvements in data processing efficiency, regulatory compliance, and customer service delivery. AI-driven solutions have introduced unprecedented accuracy in fraud detection, risk assessment, and market tre

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Researcher. "AI AND CLOUD INTEGRATION: TRANSFORMING CRM PRACTICES." International Journal of Computer Engineering and Technology (IJCET) 15, no. 6 (2024): 197–210. https://doi.org/10.5281/zenodo.14055235.

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This comprehensive article examines the transformative impact of artificial intelligence and cloud computing integration in Customer Relationship Management (CRM) systems, highlighting key technological advancements, implementation strategies, and business outcomes. The article, drawing from studies spanning multiple organizations across numerous countries, reveals significant improvements in operational efficiency, with AI-enhanced CRM systems demonstrating substantial enhancements in response times, first-contact resolution rates, and customer lifetime value. The article explores critical as

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Muhammed-Amin, Dlvan O. "Emerging Trends in Applied Mathematics." Cihan University-Erbil Scientific Journal 8, no. 1 (2024): 36–40. http://dx.doi.org/10.24086/cuesj.v8n1y2024.pp36-40.

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This research explores the emerging trends in applied mathematics and their far-reaching implications in various fields. Machine learning and artificial intelligence are revolutionizing healthcare, finance, and natural language processing. Big data analysis is enhancing decision-making in finance, healthcare, and logistics. Quantum computing promises to transform materials science and renewable energy. These trends are reshaping research and practice, offering innovative solutions and opportunities for interdisciplinary collaboration. Ethical considerations and the development of advanced algo

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Sadanandam Meenugu. "AI and ML in payroll automation: A technical perspective." World Journal of Advanced Engineering Technology and Sciences 15, no. 1 (2025): 1542–52. https://doi.org/10.30574/wjaets.2025.15.1.0379.

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Artificial Intelligence and Machine Learning technologies are fundamentally transforming payroll management across global organizations, moving beyond basic automation toward intelligent systems capable of learning and optimization. These advanced computational approaches address traditional payroll challenges including error reduction, compliance management, and processing efficiency across diverse regulatory environments. The article explores the technical architecture underlying AI-powered payroll systems, examining the multi-layered frameworks that enable sophisticated data processing and

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Sathya, P., N. Nirmala, and T. Priyadharshini. "The Future of AI: Emerging Trends and Innovations." ComFin Research 13, S1-i2 (2025): 207–12. https://doi.org/10.34293/commerce.v13is1-i2.8767.

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AI has the potential to completely transform a number of industries, including healthcare, banking, manufacturing, and education. Driven by developments in robotics, computer vision, deep learning, and natural language processing (NLP), artificial intelligence (AI) has enormous promise when combined with cutting-edge technologies like 5G, blockchain, and quantum computing. The significance of guaranteeing openness, equity, and responsibility in AI systems is highlighted by the emergence of explainable AI (XAI) and the growing emphasis on ethical AI frameworks. With a focus on data privacy, hum

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Vinay Sai Kumar Goud Gopigari. "AI-Driven Healthcare Integration: A Comprehensive Technical Analysis of Diagnostic Transformation." Journal of Computer Science and Technology Studies 7, no. 3 (2025): 410–15. https://doi.org/10.32996/jcsts.2025.7.3.47.

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The integration of artificial intelligence in healthcare represents a transformative shift in medical diagnostics and patient care delivery. This technological evolution encompasses advanced computational architectures for medical imaging interpretation, predictive analytics for risk stratification, and natural language processing for medical information extraction. The implementation of AI-driven patient interaction systems has enhanced healthcare communication while addressing critical challenges in data interoperability and ethical considerations. The advancement of performance optimization

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Iswarya Konasani. "AI-Driven Risk Management: Transforming Financial Decision-Making." International Journal of Scientific Research in Computer Science, Engineering and Information Technology 11, no. 1 (2025): 3574–82. https://doi.org/10.32628/cseit251112382.

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The integration of artificial intelligence in financial risk management represents a transformative advancement in the industry, revolutionizing traditional approaches to credit scoring, underwriting, and risk assessment. This comprehensive article examines the implementation of AI-driven systems across financial institutions, highlighting significant improvements in operational efficiency, decision accuracy, and risk prediction capabilities. The article explores the evolution of machine learning models, advanced analytics, and automated systems that have enhanced portfolio management and regu

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Rahul Bhatia. "The Future of SIEM: How AI and ML Are Rewriting Threat Detection." Journal of Computer Science and Technology Studies 7, no. 7 (2025): 459–68. https://doi.org/10.32996/jcsts.2025.7.7.50.

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Security Information and Event Management (SIEM) systems have undergone a fundamental transformation through the integration of artificial intelligence and machine learning technologies. This article traces the evolution from traditional rule-based detection methods to sophisticated AI-enhanced platforms capable of identifying complex attack patterns. Modern SIEM solutions leverage deep learning architectures, unsupervised anomaly detection, behavioral analytics, and natural language processing to overcome historical limitations. Real-world implementations demonstrate significant operational i

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STAROVOYT, Tania, and Yuriy ZAYCHENKO. "A HYBRID QUANTUM-PERFECTED MODEL OF ARTIFICIAL INTELLIGENCE IN THE PROBLEM OF AUTOMATIC RECOGNITION AND FAST CONVERSION OF UNSTRUCTURED TEXT INFORMATION INTO SPATIAL." Advanced Information Technology, no. 1 (2) (2023): 38–48. http://dx.doi.org/10.17721/ait.2023.1.06.

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Background. Efficiently converting large amounts of unstructured text data into spatial information is crucial for managing water distribution systems. This allows for the conversion of extensive sets of text information, such as reports, orders, letters, and other documents, into point classes of spatial objects in geographic information systems. To tackle this challenge, a promising new approach involves combining hybrid quantum-classical neural networks with geo-information technologies. Methods. The study utilized quantum-enhanced hybrid neural networks in combination with GIS methods to i

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