Machine Learning and Quantum

Kaushal Kishor

Quantum machine learning, which combines quantum computers with regular machine learning, is a much contested field of research. The goal of quantum ML is to provide light on the possible applications of quantum mechanical findings to ML challenges. The exponential growth of data that must be taught to a typical computer paradigm implies that the present cutting-edge computing equipment cannot keep up with the pace. Quantum computing might be useful in this context for continuous training with enormous datasets. Scientists have resorted to quantum machine learning to reduce the development time for new learning algorithms. Traditional machine learning seeks patterns in data and uses those patterns to forecast the future. This is machine learning’s ultimate objective. However, the new patterns created by quantum techniques imply that quantum computers may be more successful in machine learning than traditional computers. One of the most significant uses of cryptography is assuring user anonymity and data security over unsecure networks. In this section, we examine the history of quantum machine learning and summarize the most current results in the field.

Kanni, Nagaraj G

Pawan Whig, Arun Velu, Rahul Reddy Nadikattu +1 more

Quantum machine learning unites two of the most contentious subfields in science: quantum computing and classical machine learning. Quantum machine learning is the study of how to solve machine learning problems using insights from quantum mechanics. As more and more information accumulates, the capacity of existing computing machines to successfully train it onto a conventional computation model is stretched to its limits. Quantum computing could be helpful for continuous training with large datasets. When developing learning algorithms, quantum machine learning aims to do so much more quickly than traditional methods. Finding patterns in data and using those patterns to predict the future is at the heart of traditional machine learning. The data can be mined for insights like these. Quantum systems, on either extreme, generate unusual patterns those conventional systems cannot, implying that quantum computers might be able to outpace traditional CPUs in ML tasks. This chapter discusses the past research on quantum machine learning and an update on its current state.

Jincheng Zhang

This paper explores the integration of quantum machine learning (QML) with classic machine learning (ML) algorithms, aiming to enhance data compression and feature extraction, ultimately improving the efficiency and accuracy of machine learning models. We delve into the synergistic potential of combining these two paradigms, focusing on the unique capabilities of quantum algorithms to address limitations inherent in traditional ML. The core mechanism involves leveraging quantum machine learning techniques to achieve superior data representation and feature identification. This research investigates the practical implications of this fusion, highlighting potential benefits and challenges in the development of advanced machine learning systems.

Claudio Conti