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IEEE International Conference on Acoustics, Speech, and Signal Processing Workshops (ICASSPW)

Word and Text Similarity Using Classical Word Embeddings in Quantum NLP Systems

Damir Ćavar, Koushik Reddy Parukola

Published Apr 6, 2025• 3 citations
Publisher

Abstract

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.

DOI
10.1109/icasspw65056.2025.11010999

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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.
We demonstrate how word and text embeddings as n-dimensional real vectors used in classical Natural Language Processing (NLP)/AI applications can be mapped to quantum states or quantum embedding representations in Quantum Applications using NLP and AI. Models like fastText [1], [2], GloVe [3], Numberbatch [4], or BERT [5] are common NLP embedding vectors or models that encode semantic properties of words or text fragments. We use these models to evaluate mapping properties and compression rate, as well as information preservation in quantum embeddings. For mapping embedding vectors to quantum states, we use encoding strategies, such as Amplitude Encoding. The encoding strategies allow us to map large dense vectors from the embedding models to compact quantum states using different compression ratios. The compression with Amplitude Encoding can be2n2^{n}2ntoNNN, resulting in a mapping of a 1,024-dimensional vector of reals in the classical environment to a 10-qubit state. The goal of this work is to evaluate these strategies with respect to their compression ratio and measure the preservation of semantic information using similarity scores. We show that the resulting quantum embeddings based on mapped classical computing embeddings exhibit the same relational properties and that there is no significant loss of semantic information in the conversion from classical n-dimensional real vector embeddings to qubit states. We conclude that the experimental results allow us to quantum compute semantic similarities of words or text, reusing existing and freely available embedding models from classical NLP/AI computing.
We present experimental results from three approaches to encoding classical text, word, or token embeddings, i.e., dense vector representations, as quantum states from state-of-the-art large language models. The experiments focus on identifying the best encoding strategies that achieve the highest compression of classical dense vectors in the number of required qubits, while maintaining the semantic properties of the embeddings trained and reflected in classical AI models. We identify the most optimal interface and data conversion approaches in hybrid classical and quantum computing systems. We demonstrate that the conversion of dense embedding vectors to Hamiltonians is a feasible strategy for a hybrid classicalquantum computing environment and storage of quantum states on classical computers. We report the experimental results of reducing the full embedding-based Hamiltonians using Principal Component Analysis (PCA).
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.
While having enhanced NLP, deep learning and pre-trained language models requires a lot of processing power.The work showcases the potential of quantum computing by mapping linguistic data into vast, high-dimensional Hilbert spaces through entanglement and superposition.It focuses on mathematical concepts that set quantum approaches apart from classical ones, among them being the fidelity-based similarity and quantum probability.Various quantum machine learning models are considered in this article, including Quantum Neural Networks and Quantum Support Vector Machines, each discussing the computational advantages in pattern recognition.In addition, it considers retrieval techniques like Grover's algorithm, showing how quantum similarity functions give better semantic search.Indeed, the comparison does show that quantum techniques might yield advantages regarding expressiveness and scalability, despite obstacles such as hardware noise and data encoding.Notwithstanding that quantum technology is still in its infancy, future improvements might advance language understanding.
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.
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