It is important to learn directly from original texts in natural language processing (NLP). Many deep learning (DP) models needing a large number of manually annotated data are not effective in deriving much information from corpora with few annotated labels. Existing methods using unlabeled language information to provide valuable messages consume considerable time and cost. Our provided sentence representation based on quantum computation (called Model I) needs no prior knowledge except word2vec. To reduce some semantic noise caused by the tensor product on the entangled words vector, two improved models (called Model II and Model III) are proposed to reduce the dimensions of the sentence embedding stimulated by Model I. The provided models are evaluated in the STS tasks of 2012, 2014, 2015 and 2016, for a total of 21 corpora. Experimental results show that using quantum entanglement and dimensionality reduction in sentence embedding yields state-of-the-art performances on semantic relations and syntactic structures. Compared to the Pearson correlation coefficient (Pcc) and mean squared error (MSE), the results of 16 out of 16 corpora are better than the results of the comparative methods.
It is important to learn directly from original texts in natural language processing (NLP). Many deep learning (DP) models needing a large number of manually annotated data are not effective in deriving much information from corpora with few annotated labels. Existing methods using unlabeled language information to provide valuable messages consume considerable time and cost. Our provided sentence representation based on quantum computation (called Model I) needs no prior knowledge except word2vec. To reduce some semantic noise caused by the tensor product on the entangled words vector, two improved models (called Model II and Model III) are proposed to reduce the dimensions of the sentence embedding stimulated by Model I. The provided models are evaluated in the STS tasks of 2012, 2014, 2015 and 2016, for a total of 21 corpora. Experimental results show that using quantum entanglement and dimensionality reduction in sentence embedding yields state-of-the-art performances on semantic relations and syntactic structures. Compared to the Pearson correlation coefficient (Pcc) and mean squared error (MSE), the results of 16 out of 16 corpora are better than the results of the comparative methods.
Sentence representation is one of the foundations of natural language processing. A quantum entanglement-based approach is provided to determine the sentence similarity. The sentence embedding based on quantum computation considers the semantic structure of a sentence, tensor product and attentive mechanism. Main components of modifiers of a complete sentence are obtained by using the attentive mechanism combining with quantum entanglement of two consecutive notional words. The semantic features and syntactic structures of sentences are extracted by the tensor product of two consecutive notional words without any big-corpus or prior knowledge. Experimental results implemented on 17 datasets with an excellent effect show that taking the quantum entanglement into the sentence embedding yields improvements on mining semantic relations and syntactic structures. These results shed new light on sentence representation with limited resources, and represent a new step towards the quantum entanglement-based application in natural language processing.
In this paper, we present an extension, and an evaluation, to existing Quantum like approaches of word embedding for IR tasks that (1) improves complex features detection of word use (e.g., syntax and semantics), (2) enhances how this method extends these aforementioned uses across linguistic contexts (i.e., to model lexical ambiguity) - specifically Question Classification -, and (3) reduces computational resources needed for training and operating Quantum based neural networks, when confronted with existing models. This approach could also be latter applicable to significantly enhance the state-of the-art across Natural Language Processing (NLP) word-level tasks such as entity recognition, part-of-speech tagging, or sentence-level ones such as textual relatedness and entailment, to name a few.
Word embeddings and vector representations for linguistic units are important in Natural Language Processing (NLP) and AI systems. Engineering of such models involves significant effort, large amounts of data, and costly computations. Many models and systems have been released to the general public, enabling significant research and development in the classical NLP domain. We demonstrate how word and text embeddings as n-dimensional dense vectors of n real numbers in classical computers can be mapped to highly compressed quantum states or quantum embedding using log(n) qubits. In similarity experiments performed on classical and quantum computers, we show no significant information loss that would affect vector distance scores representing words’ semantic similarity. The results show that existing NLP embedding models in classical computing environments can be used in quantum computing. We discuss the issues and limitations of this approach in the context of current quantum computing environments.
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.
Abstract In the era of Large Language Models, there is still potential for improvement in current Natural Language Processing (NLP) methods in terms of verifiability and consistency. NLP classical approaches are computationally expensive due to their high-power consumption, computing power, and storage requirements. Another computationally efficient approach to NLP is categorical quantum mechanics, which combines grammatical structure and individual word meaning to deduce the sentence meaning. As both quantum theory and natural language use vector space to describe states which are more efficient on quantum hardware, QNLP models can achieve up to quadratic speedup over classical direct calculation methods. In recent years, there is significant progress in utilizing quantum features such as superposition and entanglement to represent linguistic meaning on quantum hardware. Earlier research work has already demonstrated QNLP’s potential quantum advantage in terms of speeding up search, enhancing classification tasks’ accuracy and providing an exponentially large quantum state space in which complex linguistic structures can be efficiently embedded. In this work, a QNLP model is used to determine if two sentences are related to the same topic or not. By comparing our QNLP model to a classical tensor network-based one, our model improved training accuracy by up to 45% and validation accuracy by 35%, respectively. The QNLP model convergence is also studied when varying: first, the problem size, second, parametrized quantum circuits used for model’s training, and last, the backend quantum simulator noise model. The experimental results show that strongly entangled ansatz designs result in fastest model convergence.
Quantum computing with its capacity to compute multiple possibilities at once, and perform some kinds of calculations almost instantly is one leading edge. The integration of QCs and DL undoubtedly represent one such transformative means for improving Natural Language Processing (NLP). By combining the two, this uses quantum principles of superposition and entanglement to make computation faster regarding human language comprehension limitations in classical computing. Deep learning models, specifically neural networks have already made significant improvements over NLP tasks like language translations, sentiment analysis and text generation. Nevertheless, these models also face scalability and computational efficiency issues with huge volumes of data. Quantum computing offers a solution to all this by reducing the time exponentially that it takes to fetch large amounts of language data and running deep learning algorithms. This paper investigates the opportunities that integrating quantum computing and deep learning into NLP practices might unlock. We drafted the fundamentals of quantum computing and why it is a need over classical computing, where we discussed on how quantum beneficial for data processing & optimization of an algorithm. We then explore applications of quantum enhanced deep learning for NLP, with a focus on the impact that training and inference can have on model performance. We demonstrate the utility of this integration by integrating our four SQDs to showcase improvements on diverse NLP tasks through a series of experiments and case studies. We also demonstrate that hybrid quantum-classical approaches allow the combination of traditional ML and quantum-enhanced models, achieving state-of-the-art performance with respect to effectiveness, scalability and precision