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IEEE International Conference on Progress in Informatics and Computing

Quantum Entanglement Based Sentence Similarity Computation

Yu Yan, Dong Qiu, Ruiteng Yan

Published Dec 18, 2020• 3 citations
Publisher

Abstract

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.

DOI
10.1109/pic50277.2020.9350744

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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.
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.
Quantum Natural Language Processing (QNLP) is coded in the semantic space using a combined semantic distribution classification model with tensor operations. Its theoretical results on quantum circuit mapping and quantum semantic coding of text have been tested in practice with the recent development of quantum back-end equipment. In view of the small scale of quantum natural language processing tasks, single sentence structure of quantum semantic coding, low text coverage and lack of application, this paper proposes a combination of syntactic structure to extract text and extend the sentence components of quantum semantic coding, which improves the utilization of text processing task of quantum computing resources. In view of the fact that quantum natural language processing has few cases in specific applications, this paper studies the application expansion possibility of quantum text matching and question answering applications. The development path of classical natural language processing is referred to enhance the usefulness and explore the practical ability of QNLP in the current resource-constrained conditions.
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To mine more semantic information between words, it is important to utilize the different semantic correlations between words. Focusing on the different degrees of modifying relations between words, this article provides a quantum-like text representation based on syntax tree for fuzzy semantic analysis. Firstly, a quantum-like text representation based on density matrix of individual words is generalized to represent the relationship of modification between words. Secondly, a fuzzy semantic membership function is constructed to discuss the different degrees of modifying relationships between words based on syntax tree. Thirdly, the tensor dot product is defined as the sentence semantic similarity by combining the operation rules of the tensor to effectively exploit the semantic information of all elements in the quantum-like sentence representation. Finally, extensive experiments on STS’12, STS’14, STS’15, STS’16 and SICK show that the provided model outperforms the baselines, especially for the data set containing multiple long-sentence pairs, which confirms there are fuzzy semantic associations between words.
Abstract To emphasize the semantic impact of local semantic and grammatical information among adjacent words in the input text, we establish a constraint functions-based quantum-like tensor compression sentence representation model by integrating the concept of extending the pure state-based density matrix to the mixed-state projection operator in quantum mechanics. The provided model highlights the semantic significance of mixed word associations in the input text, simultaneously reducing the reliance on information derived solely from dictionary statistics. We combine the correlation coefficient with the attention mechanism to establish the correlation coefficient between words. The quantum-like sentence representation based on pure state density matrix is extended to the projection operator of mixed states. Combining the acquisition of maximum in convex optimization, a constraint functions-based quantum-like text representation pruning model is established to reduce redundant information caused by dimensional expansion of tensor operations. The experimental results on SICK-2014, STS-benchmark, and STS-companion show that the provided model is more effective than the mainstream models in mining semantic information, especially more sensitive to the negative semantics of sentences.
We present a quantum computing approach to analyzing Large Language Model (LLM) embeddings, leveraging complexvalued representations and modeling semantic relationships using quantum mechanical principles. By establishing a direct mapping between LLM semantic spaces and quantum circuits, we demonstrate the feasibility of estimating semantic similarity using quantum hardware. One of the key results is the experimental calculation of cosine similarity between Google Sentence Transformer embeddings using a real quantum computer, providing a tangible demonstration of a quantum approach to semantic analysis. This work reveals a connection between LLMs and quantum mechanics, suggesting that these principles can offer new perspectives on semantic representation and processing, and paving the way for future development of quantum algorithms for natural language processing.