Machine Learning with Quantum Computers

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.

Dipesh Niraula, Jamalina Jamaluddin, Julia M. Pakela +1 more

Esma Aı̈meur, Gilles Brassard A, Sébastien Gambs

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.

Pravin Ghosekar

The quantum computing paradigm is revolutionizing computation with the use of quantum mechanical principles such as superposition and entanglement. The machine learning (ML) method has also emerged as a cornerstone of modern artificial intelligence. Quantum Machine Learning (QML) combines quantum computing with machine learning to achieve unprecedented computational speed and efficiency. Quantum computing, machine learning, and its potential to transform computing technologies in the future are explored in this paper. Furthermore, it discusses key application areas, advantages, challenges, and future directions for research. QML can significantly improve data processing, optimization, and predictive modeling, paving the way for the next generation of intelligent systems, according to the study.

Pengfei Zhang