Quantum-Computing-Inspired Algorithms in Machine Learning

View Sample PDF

Author(s): Deeksha Kaul (VIT University, India), Harika Raju (VIT University, India)and B. K. Tripathy (VIT University, India)
Copyright: 2021
Pages: 20
Source title: Research Anthology on Artificial Intelligence Applications in Security
Source Author(s)/Editor(s): Information Resources Management Association (USA)
DOI: 10.4018/978-1-7998-7705-9.ch021

Keywords: Artificial Intelligence / Computer Science & IT / Cyber and Network Security / Information Science Reference

Purchase

View Quantum-Computing-Inspired Algorithms in Machine Learning on the publisher's website for pricing and purchasing information.

Abstract

In this chapter, the authors discuss the use of quantum computing concepts to optimize the decision-making capability of classical machine learning algorithms. Machine learning, a subfield of artificial intelligence, implements various techniques to train a computer to learn and adapt to various real-time tasks. With the volume of data exponentially increasing, solving the same problems using classical algorithms becomes more tedious and time consuming. Quantum computing has varied applications in many areas of computer science. One such area which has been transformed a lot through the introduction of quantum computing is machine learning. Quantum computing, with its ability to perform tasks in logarithmic time, aids in overcoming the limitations of classical machine learning algorithms.

The IRMA Community

Research IRM

Quantum-Computing-Inspired Algorithms in Machine Learning

Purchase

Abstract

Related Content

IRMA Sponsors