[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.
[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.
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.
Machine Learning (ML) has been widely applied across numerous domains due to its ability to automatically identify informative patterns from data for various tasks. The availability of large-scale data and advanced computational power enables the development of sophisticated models and training strategies, leading to state-of-the-art performance, but it also introduces substantial challenges. Quantum Computing (QC), which exploits quantum mechanisms for computation, has attracted growing attention and significant global investment as it may address these challenges. Consequently, Quantum Machine Learning (QML), the integration of these two fields, has received increasing interest, with a notable rise in related studies in recent years. We are motivated to review these existing contributions regarding quantum circuit-based learning models for classical data analysis and highlight the identified potentials and challenges of this technique. Specifically, we focus not only on QML models, both kernel-based and neural network-based, but also on recent explorations of their integration with classical machine learning layers within hybrid frameworks. Moreover, we examine both theoretical analysis and empirical findings to better understand their capabilities, and we also discuss the efforts on noise-resilient and hardware-efficient QML that could enhance its practicality under current hardware limitations. In addition, we cover several emerging paradigms for advanced quantum circuit design and highlight the adaptability of QML across representative application domains. This study aims to provide an overview of the contributions made to bridge quantum computing and machine learning, offering insights and guidance to support its future development and pave the way for broader adoption in the coming years.
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.
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 the superposition and entanglement properties of quantum states, potentially revolutionizing how semantic meaning is extracted and processed. The primary objective of this project is to develop a QNLP framework capable of performing semantic analysis on autogenerated sentences. We will investigate the application of quantum algorithms and quantum machine learning models to decode the semantic structure and context of sentences generated by language models. This involves encoding textual data into quantum states, applying quantum gates and circuits to manipulate these states, and measuring the outcomes to interpret semantic information. By integrating quantum computing with NLP, this project seeks to achieve higher accuracy and efficiency in understanding complex language patterns. The anticipated outcomes include a comprehensive evaluation of QNLP's performance in semantic analysis, a comparison with classical NLP methods, and potential advancements in language processing tasks such as sentiment analysis, language translation, and text summarization. This project will contribute to the growing body of research at the intersection of quantum computing and artificial intelligence, paving the way for innovative solutions in the field of natural language processing.
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.
Quantum Machine Learning represents a paradigm shift at the intersection of Quantum Computing and Machine Learning, leveraging quantum phenomena such as superposition, entanglement, and quantum parallelism to address the limitations of classical approaches in processing high-dimensional and large-scale datasets. This survey provides a comprehensive analysis of Quantum Machine Learning, detailing foundational concepts, algorithmic advancements, and their applications across domains such as healthcare, finance, and quantum chemistry. Key techniques, including Quantum Support Vector Machine, Quantum Neural Network, Quantum Decision Trees, and hybrid quantum-classical models, are explored with a focus on their theoretical foundations, computational benefits, and comparative performance against classical counterparts. While the potential for exponential speedups and enhanced efficiency is evident, the field faces significant challenges, including hardware constraints, noise, and limited qubit coherence in the current era of Noisy Intermediate-Scale Quantum devices. Emerging solutions, such as error mitigation techniques, hybrid frameworks, and advancements in quantum hardware, are discussed as critical enablers for scalable and fault-tolerant Quantum Machine Learning systems. By synthesizing state-of-the-art developments and identifying research gaps, this survey aims to provide a foundational resource for advancing Quantum Machine Learning toward practical, real-world applications in tackling computationally intensive problems.