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Background/Objectives: Information is crucial in present world; text is one form of information that is being exchanged in alarming rates. Natural language Processing is one field that concentrates on text analysis. Methods/Statistical analysis: Text Analyzers collects the word vectors and embed them into one by calculating semantics, and their relationships were considered on bases of dependencies and dependency trees which only targets subject to object relations and vice versa. In this digital era, microblogs involve more complicated text which are very hard using dependencies and relations to comprehend in bases of contextual semantics. Findings: In this paper we are addressing this problem by building a novel quantum enhanced modal. The proposed methodology exchange parameters between NLP algorithm and Quantum native optimizer allowing us to solve non-linear problems while composing the semantics. Improvements/Applications: We have integrated our methodology into a simple question and answering system for assessment, this system will give us the scores and answers build upon context already existing on the internet. In every Assessment Quantum Trained or Q-Trained algorithm exhibited promising results when compared with the best-in-class NLP algorithm ALBERT.