Quantum Machine Learning Algorithms for Diagnostic Applications: A Review

Abstract

Big data analytics is a huge information investigation in the utilization of cutting edge scientific procedures against extremely enormous, various in-formational collections that incorporate organized, semi-organized and unstructured information, from various sources, and in various sizes from terabytes to zettabytes. Big data is in many formats such as text, photographs, videos, and so on. Big data goes beyond the storage and processing capability of a traditional computer. Accordingly, distinguishing designs in enormous information is extremely challenging. A classical computing measures a lot of information over a long measure of time. Quantum computing is a new research area where information and data are computed in a short measure of time. This is a survey study that glances at how enormous information and quantum machine learning can be utilized to distinguish information designs for a collection of utilizations. The advantages of quantum guided and unsupervised machine learning are contrasted to those of classical computing. Quantum computing in machine learning algorithms: challenges and techniques are also discussed. Despite the fact that quantum machine learning is a promising area, several real-world applications have been discussed.