Application of Quantum Natural Language Processing for Language Translation

In this paper, we develop compositional vector-based semantics of positive transitive sentences using quantum natural language processing (Q-NLP) to compare the parametrized circuits two synonymous simple in English and Persian. We propose a protocol based on long short-term memory (Q-LSTM) for Q-NLP perform various tasks general but specifically translating sentence from Then, generalize our method use as an input Q-LSTM cell. This enables us translate different languages. Our work paves way toward representing neural machine translation, which may demonstrate quadratic speedup converge faster or reaches better accuracy over classical methods.