Quantum semantic coding for structure extraction and matching applications

Abstract

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.