Classical Equivalent Quantum Unsupervised Learning Algorithms
Prakhar Shrivastava, Kapil Kumar Soni, Akhtar Rasool · Procedia Computer Science · 2020
The paper presents necessity of data pre-processing to process the data set through machine learning algorithms, and it filters the raw data to make the data as in compatible format for analysis purpose. The methods such as mean normalization, feature scaling and dimensionality reduction are used for data smoothing and therefore formatted data set gets processed by learning algorithms to predict best possible outcome as per the analysis. Machine learning became famous for such processing purpose and through unsupervised learning we are capable of processing over various data formats. Such algorithms performance is up-to the mark, but as per the availability of inherent parallel processing through quantum machines, computational speedup can be achieved by designing classical equivalent quantum machine learning algorithms. In this paper, we discussed some of the classical unsupervised learning algorithms, and then we propose the equivalent quantum version of algorithms along with the mathematical justification over the complexity analysis and achieved computational speedup and show the betterment by processing such problems over quantum machines.