Paper Title
Detect and Implement RNA Sequence using Convolutional Neural Networks in Quantum Machine Learning

pseudouridine is the most predominant RNA alteration and has been found in the two eukaryotes and prokaryotes. Presently, pseudouridine has been exhibited in a few sorts of RNAs, for example, little atomic RNA, rRNA, tRNA, mRNA, and little nucleolar RNA. Hence, its hugeness to scholastic research and medication improvement is reasonable. Through biochemical tests, the pseudouridine site recognizable proof has created great results, however these lab exploratory strategies and biochemical procedures are costly and time devouring. In this way, it is imperative to present effective strategies for ID of pseudouridine locales. In this investigation, a keen technique for pseudouridine locales utilizing the deeplearning approach was created. The proposed expectation model is called iPseU-CNN (recognizing pseudouridine by convolutional neural systems). The current techniques utilized handmade highlights and AI ways to deal with recognize pseudouridine locales. Be that as it may, the proposed indicator removes the highlights of the pseudouridine locales naturally utilizing a convolution neural system model. The iPseU-CNN model yields preferable results over the present best in class models in all assessment parameters. It is in this way exceptionally anticipated that the iPseU-CNN indicator will turn into an accommodating instrument for scholastic research on pseudouridine site forecast of RNA, just as in tranquilize revelation. Keywords - Quantum Machine Learning, Neural Network, RNA Sequence.