The convergence of quantum computing and large language models (LLMs) represents a major milestone in computational intelligence, introducing novel paradigms for information processing, complexity modeling, and scientific advancement. This survey provides a comprehensive overview of the emerging interdisciplinary field that integrates quantum mechanics with natural language processing. We begin by presenting the foundational principles of quantum computing and LLMs, then examine their integration through automated quantum code generation, hybrid transformer architectures, and quantum natural language processing (QNLP) frameworks. State-of-the-art models, such as quantum-augmented attention mechanisms and circuit embedding strategies, are evaluated across domain-specific datasets and benchmarks. We also investigate the role of LLMs as quantum research assistants, educational tools, and simulation agents, particularly in resource-constrained environments. Furthermore, the survey explores challenges related to scalability, interpretability, and quantum data encoding, and outlines future research directions-especially the vision for fully quantum-native LLMs. Our goal is to guide researchers through the current landscape and to encourage continued exploration at the intersection of quantum technologies and advanced language models.
The convergence of quantum computing and large language models (LLMs) represents a major milestone in computational intelligence, introducing novel paradigms for information processing, complexity modeling, and scientific advancement. This survey provides a comprehensive overview of the emerging interdisciplinary field that integrates quantum mechanics with natural language processing. We begin by presenting the foundational principles of quantum computing and LLMs, then examine their integration through automated quantum code generation, hybrid transformer architectures, and quantum natural language processing (QNLP) frameworks. State-of-the-art models, such as quantum-augmented attention mechanisms and circuit embedding strategies, are evaluated across domain-specific datasets and benchmarks. We also investigate the role of LLMs as quantum research assistants, educational tools, and simulation agents, particularly in resource-constrained environments. Furthermore, the survey explores challenges related to scalability, interpretability, and quantum data encoding, and outlines future research directions-especially the vision for fully quantum-native LLMs. Our goal is to guide researchers through the current landscape and to encourage continued exploration at the intersection of quantum technologies and advanced language models.
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.
Large Language Models (LLMs) such as GPT and LLaMA have transformed artificial intelligence, but their rapid growth has brought major challenges in computation, energy use, and scalability. Quantum computing (QC) offers a fundamentally different way to process information, with the potential to accelerate key operations in training, inference, optimization, and representation learning. This survey provides a structured and comprehensive review of how QC can support the future development of LLMs. It examines quantum algorithms, hybrid quantum-classical methods, quantum neural networks, quantum embeddings, and security applications. It also evaluates their feasibility in both the current noisy intermediate-scale quantum (NISQ) era and the future fault-tolerant era. The survey highlights current progress, identifies major technical barriers, and outlines practical research directions needed to build scalable, efficient, and secure quantum-enhanced language models.
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.
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 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.
Code Large Language Models (Code LLMs) have emerged as powerful tools, revolutionizing the software development landscape by automating the coding process and reducing time and effort required to build applications. This paper focuses on training Code LLMs to specialize in the field of quantum computing. We begin by discussing the unique needs of quantum computing programming, which differ significantly from classical programming approaches or languages. A Code LLM specializing in quantum computing requires a foundational understanding of quantum computing and quantum information theory. However, the scarcity of available quantum code examples and the rapidly evolving field, which necessitates continuous dataset updates, present significant challenges. Moreover, we discuss our work on training Code LLMs to produce high-quality quantum code using the Qiskit library. This work includes an examination of the various aspects of the LLMs used for training and the specific training conditions, as well as the results obtained with our current models. To evaluate our models, we have developed a custom benchmark, similar to HumanEval, which includes a set of tests specifically designed for the field of quantum computing programming using Qiskit. Our findings indicate that our model outperforms existing state-of-the-art models in quantum computing tasks. We also provide examples of code suggestions, comparing our model to other relevant code LLMs. Finally, we introduce a discussion on the potential benefits of Code LLMs for quantum computing computational scientists, researchers, and practitioners. We also explore various features and future work that could be relevant in this context.