Enhanced Machine Learning using Quantum Computing

Abstract

In recent times, increasing amount of the data have enriched the decision making using machine learning. Despite of the growth in the domain of machine learning, the proximity to the physical limits of chip fabrication in classical computing is motivating researchers to explore the properties of quantum computing. A few research efforts have demonstrated that quantum computers which leverages the properties of quantum mechanics, carries the ability to surpass classical computers in machine learning tasks. The study in this paper contributes in enabling researchers to understand how quantum computers can bring a paradigm shift in the field of machine learning. The paper focuses on the concepts of quantum computing that would be used by any machine learning technique to facilitate quantum machine learning. It also studies different quantum algorithms that could be used as core subroutines in machine learning techniques and highlights the famous Grovers algorithm. These subroutines are used to enhance the efficacy of machine learning tasks. The paper towards the end advocates the use of quantum application software and throws light on the existing challenges faced by quantum computers in the current scenario.