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Apple Academic Press eBooks

Quantum Computing to Enhance Performance of Machine Learning Algorithms

Shiwani Gupta, Namrata D. Deshmukh

Published Jul 4, 2022• 1 citation
Publisher

Abstract

The field of quantum computing aims to speed up the solution for certain computational problems using quantum computers. The key milestone in this field is the claim that quantum supremacy can be achieved using quantum computers which shows that the quantum speedup is achievable in a real time system. Quantum computing can improve the performance of machine learning algorithms based on the quantum algorithms by producing better solutions for machine learning tasks than classical machine learning approaches. This chapter provides an overview on quantum computing, quantum supremacy and recent research and inventions in the field. The paper additionally illustrates the research conducted with respect to impact of quantum computing on machine learning.

DOI
10.1201/9781003277217-13

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Quantum machine learning, a cutting-edge topic of study, brings together the frontiers of quantum computing with classical machine learning. Quantum computing and machine learning may be able to help solve problems in each other's fields of expertise. Due to the surge of new data, there is growing fear that current machine learning algorithms may be exceeding their computational constraints. Thus, in certain machine learning tasks, quantum computing can be an advantage. Quantum machine learning research focuses on the creation and deployment of quantum software that could speed up machine learning in comparison to standard computing systems. Because of improvements in computing power and algorithmic innovation, machine learning approaches have emerged as powerful tools for detecting patterns in data. Because quantum systems exhibit unusual patterns that classical systems are unable to produce efficiently, it is plausible to assume that quantum computers will outperform classical computers on machine learning tasks. The following is a summary of the current state of quantum machine learning research.
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The computing sector has undergone radical changes in the last few decades. Conventional computers use binary (1s and 0s) numbers to perform tasks that are specified by the user. Using the concepts of quantum physics, a unique method known as “quantum computing” addresses problems that are too complicated for conventional computing devices. Two of the scientific fields with the fastest growth rates in today's time are machine learning and quantum computing. Researchers have recently initiated investigations into the ways in which conventional machine learning methods can be improved by quantum computing. Quantum machine learning comprises hybrid approaches that combine quantum and classical algorithms, using quantum methods to evaluate quantum states rather than traditional data. Current data science techniques could be significantly improved by quantum algorithms. In this work, we summarize the contributions made by different researchers in the domain of Quantum Learning and then look at some of the methods related to its real-world uses.
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