Language model is a fundamental research topic in areas related to natural language processing. In recent years, researchers have proposed quantum language models based on the probability theory of quantum mechanics. This paper aims to review the research motivation and the current progress of constructing various quantum language models. First, it reviews the research problems of classical language models. Then, it introduces some quantum language models in information retrieval and speech processing, as well as an end-to-end quantum language model based on neural network architecture. By analyzing the advantages and disadvantages of each quantum language model considered here, taking into account the essential connection between quantum mechanics and neural networks, we outline our vision for future research directions.
Language model is a fundamental research topic in areas related to natural language processing. In recent years, researchers have proposed quantum language models based on the probability theory of quantum mechanics. This paper aims to review the research motivation and the current progress of constructing various quantum language models. First, it reviews the research problems of classical language models. Then, it introduces some quantum language models in information retrieval and speech processing, as well as an end-to-end quantum language model based on neural network architecture. By analyzing the advantages and disadvantages of each quantum language model considered here, taking into account the essential connection between quantum mechanics and neural networks, we outline our vision for future research directions.
Recent research has investigated quantum NLP, designing algorithms that process natural language in quantum computers, and also quantum-inspired algorithms that improve NLP performance on classical computers.In this survey, we review representative methods at the intersection of NLP and quantum physics in the past ten years, categorizing them according to the use of quantum theory, the linguistic targets that are modeled, and the downstream application.The literature review ends with a discussion on the key factors to the success that has been achieved by existing work, as well as challenges ahead, with the goal of better understanding the promises and further directions.
Language Modeling (LM) is a fundamental research topic in a range of areas. Recently, inspired by quantum theory, a novel Quantum Language Model (QLM) has been proposed for Information Retrieval (IR). In this paper, we aim to broaden the theoretical and practical basis of QLM. We develop a Neural Network based Quantum-like Language Model (NNQLM) and apply it to Question Answering. Specifically, based on word embeddings, we design a new density matrix, which represents a sentence (e.g., a question or an answer) and encodes a mixture of semantic subspaces. Such a density matrix, together with a joint representation of the question and the answer, can be integrated into neural network architectures (e.g., 2-dimensional convolutional neural networks). Experiments on the TREC-QA and WIKIQA datasets have verified the effectiveness of our proposed models.
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.
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 theory, originally proposed as a physical theory to describe the motions of microscopic particles, has been applied to various non-physics domains involving human cognition and decision-making that are inherently uncertain and exhibit certain non-classical, quantum-like characteristics. Sentiment analysis is a typical example of such domains. In the last few years, by leveraging the modeling power of quantum probability (a non-classical probability stemming from quantum mechanics methodology) and deep neural networks, a range of novel quantum-cognitively inspired models for sentiment analysis have emerged and performed well. This survey presents a timely overview of the latest developments in this fascinating cross-disciplinary area. We first provide a background of quantum probability and quantum cognition at a theoretical level, analyzing their advantages over classical theories in modeling the cognitive aspects of sentiment analysis. Then, recent quantum-cognitively inspired models are introduced and discussed in detail, focusing on how they approach the key challenges of the sentiment analysis task. Finally, we discuss the limitations of the current research and highlight future research directions.
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.