Analysis on quantum reinforcement learning algorithms for prediction of protein sequence.

Protein structure expectation is a particularly mind boggling issue that it is frequently assaulted and disintegrated using four distinct levels and they are: 1-D forecast of under- lying highlights along the essential succession of amino acids sequences, 2-D forecast of spatial connections between the sequence of amino acids, 3-D forecast of a tertiary structure of protein and quaternary structure of protein. This paper also try to introduce some assessment tools for finding the accuracy of result from applying ML and DL tools. And try to analyses and compare various algorithms based on deep learning methods verses machine learning methods used for sequence prediction. This paper also examines the turn of events and utilization of concealed Markov model, uphold vector machines, Bayesian techniques, and grouping strategies. This investigation will be helpful in creating future strategies to improve the exactness of protein auxiliary structure expectation. In this paper, also introduce and summarize the problem of quantum essential elements of: (1) Variational auto-encoder (2) GAN, generative adversarial network (3) RNN, recurrent neural (4) CNN, convolutional neural networks protein structure prediction. Later on also summarizes the evolution of predictive algorithms for 1-4D structure of protein from Amino Acid Sequences and summarize the deep learning ideas to prediction of structure of protein and learned algorithms of the last decade. [ABSTRACT FROM AUTHOR]