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XRDS Crossroads The ACM Magazine for Students

Quantum algorithms for machine learning

Bingjie Wang

Published Sep 20, 2016• 2 citations
Publisher

Abstract

Quantum computing and machine learning are two technologies that have generated unparalleled amounts of hype among the scientific community and popular press. Both are mysterious, immensely powerful, and on a collision course with each other.
DOI
10.1145/2983535

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Innovation in machine learning is far from complete.In fact, things are just about to take a 'quantum leap', when the world of quantum physics and machine learning are about to merge, aiming to solve advanced problems through intelligent computing.Quantum-classical hybrid techniques termed "variational algorithms" are well-suited to noisy quantum computing devices and are swiftly gaining popularity in the quantum machine learning community.Quantum machines tap directly into an unimaginably vast fabric of reality-the unusual and counterintuitive realm of quantum field theory, to accelerate computation.Quantum computers will predictively play an important role in machine learning, including the essential element of accessing more computationally complex feature spaces.
Abstract Here we will give a perspective on new possible interplays between machine learning and quantum physics, including also practical cases and applications. We will explore the ways in which machine learning could benefit from new quantum technologies and algorithms to find new ways to speed up their computations by breakthroughs in physical hardware, as well as to improve existing models or devise new learning schemes in the quantum domain. Moreover, there are lots of experiments in quantum physics that do generate incredible amounts of data and machine learning would be a great tool to analyze those and make predictions, or even control the experiment itself. On top of that, data visualization techniques and other schemes borrowed from machine learning can be of great use to theoreticians to have better intuition on the structure of complex manifolds or to make predictions on theoretical models. This new research field, named as quantum machine learning, is very rapidly growing since it is expected to provide huge advantages over its classical counterpart and deeper investigations are timely needed since they can be already tested on the already commercially available quantum machines.
A significant amount of interest has been generated in recent years in the convergence of quantum computing and data mining due to quantum algorithms' potential to revolutionize information extraction from vast datasets, as well as their ability to utilize quantum principles and natural capabilities to perform far more efficiently and efficiently than classic algorithms. We provide a comprehensive and detailed review of the current state of quantum computing algorithms in the field of data mining and machine learning, highlighting key developments, challenges, and their implementation. In particular, machine learning can benefit from QC mechanics like superposition and entanglement due to the use of q-bits as well as other superior advantages provided by quantum computing, such as Grover algorithm. It is important to note that classic machine learning models differ in terms of the types of learning, prediction models, and operating on data as well as performance over time, along with their counterparts, such as supervised and unsupervised models, which are naturally more sophisticated. Our review will also discuss how integrating and merging the two fields of machine learning and quantum computing, and their mechanics, will affect the aspect of time and resource
Machine learning is a branch of artificial intelligence that is being used at a large scale to solve science, engineering, and medical tasks. Quantum computing is an emerging technology that has a very high computational ability to solve complex problems. Classical machine learning with traditional systems has some limitations for problem-solving due to a large amount of data availability. Quantum machine learning has high performance and computational ability that can effectively be used to solve computation tasks. This study reviews the latest articles in quantum computing and quantum machine learning. Building blocks of quantum computing and different flavors of quantum algorithms are also discussed. The latest work in quantum neural networks is also presented. In the end, different possible applications of quantum computing are also discussed.
Quantum technologies can provide innovative solutions to many complex problems, and thus quantum machine learning has taken a unique place in the world of computing. Quantum technology reaches an advanced level when the potential of quantum computing features is used for machine learning. Applying quantum computing features in traditional algorithms provides an exceptional parallel computing capability for solving complex problems. The essence of this paper is a comparative study of the basic concepts of quantum computing and their superior capabilities over classical computing. This article describes the application based algorithms such as QSVM, QPCA, and Q-KNN along with Grover's algorithm, which is the most popular and fundamental quantum machine learning algorithm. This study aims to understand various learning models that incorporate the advantages of computing into quantum circuits for enhancing classical machine learning functionalities.