Quantum Machine Learning: A Review and Current Status

Published in Springer Advances in Intelligent Systems and Computing, 2020


Authors: Nimish Mishra, Manik Kapil, Hemant Rakesh, Amit Anand, Nilima Mishra, Aakash Warke, Soumya Sarkar, Sanchayan Dutta, Sabhyata Gupta, Bikash K Behera, Prasanta K Panigrahi, et alia

Abstract: Quantum machine learning is at the intersection of two of the most sought after research areas—quantum computing and classical machine learning. Quantum machine learning investigates how results from the quantum world can be used to solve problems from machine learning. The amount of data needed to reliably train a classical computation model is evergrowing and reaching the limits which normal computing devices can handle. In such a scenario, quantum computation can aid in continuing training with huge data. Quantum machine learning looks to devise learning algorithms faster than their classical counterparts. Classical machine learning is about trying to find patterns in data and using those patterns to predict further events. Quantum systems, on the other hand, produce atypical patterns which are not producible by classical systems, thereby postulating that quantum computers may overtake classical computers on machine learning tasks. Here, we review the previous literature on quantum machine learning and provide the current status of it.