Data encodings and algorithms for quantum embedding of data

Encoding classical data into a quantum is some of the most crucial steps to get to a fully functioning quantum computer but to this day there is no data encoding which is optimal for every use case this paper tries to show different data encodings for different use cases with their advantages and disadvantages the paper also shows algorithms that are used to implement those encodings in reality the paper divides the encoding in two different categories fixed and trainable encodings we discuss both the encoding in detail along with their respective use cases we also discuss about data encoding on NISQ(noisy intermediate quantum) computers the paper also discuss the machine learning approach to quantum embedding of data and its advantages along with its use cases in classification tasks and other machine learning task