Machine Translation using Quantum Neural Network for Simple Sentences

This paper presents the machine translation system (MTS) which is based on the concept of self learning of semantically correct corpus using pattern recognition. The self learning process using pattern recognition is based on Quantum Neural Network (QNN). This is a novel and new approach to recognize and learn the corpus pattern using QNN. The paper 9ppresents systematically structure of the system, machine translation system and performance results. Present procedure performs the task of translation using its knowledge gained during learning by inputting pair of sentences from source to target language. Like a person, the system also acquires the necessary knowledge required for translation in implicit form from inputting pair sentences. The performance is also compared with other ANN approaches. It has also been shown that QNN requires less training time than the traditional ANN based training.