Pattern can be learned from existing data to provide sense to the previously unknown data, this is possible only with the help of Machine learning. As part of both artificial intelligence and statistics, machine learning algorithms process large amounts of information. Machine learning refers to usage of computers to infer data based on knowledge. In the big data analysis huge amount of update data should be deal with the highly sophisticated machine learning algorithms . Quantum computing may improve classical machine learning algorithms if we deal with big data. The quantum computer works on the principle of translating stochastic methods into the language of quantum theory. This paper is focusing on the brief review on quantum computers and machine learning.
Pattern can be learned from existing data to provide sense to the previously unknown data, this is possible only with the help of Machine learning. As part of both artificial intelligence and statistics, machine learning algorithms process large amounts of information. Machine learning refers to usage of computers to infer data based on knowledge. In the big data analysis huge amount of update data should be deal with the highly sophisticated machine learning algorithms . Quantum computing may improve classical machine learning algorithms if we deal with big data. The quantum computer works on the principle of translating stochastic methods into the language of quantum theory. This paper is focusing on the brief review on quantum computers and machine learning.
Massive amounts of data in many forms, including text, images, video, and more, are referred to as big data. The storage and processing capabilities of a conventional computer can’t keep up with the demands of big data. As a result, it’s tough to spot patterns in large amounts of data. Large quantities of data take a typical computer a long time to process. Using quantum computing, data and information may be calculated quickly. Using big data with quantum machine learning to detect data patterns for a range of applications is the subject of this review research. When compared to traditional computers, quantum-guided and unsupervised machine learning have distinct benefits. The challenges, future scope, and techniques of quantum computing with machine learning algorithms are also addressed. Some real-world applications have been suggested, even if quantum machine learning is still an emerging field. This means that machine learning is being considered for industrial applications, and several studies have cited machine learning as one of the key enablers to move traditional production systems up to today’s high-tech “Industry 4.0” standards. Professionals in this sector will be able to gain a better understanding of the subject matter through the presentation and organization of this complete review.
Big Data is a term which denotes data that is beyond storage capacity and processing capabilities of classical computer and getting some insight from large amount of data is a very big challenge at hand. Quantum Computing comes to rescue by offering a lot of promises in information processing systems, particularly in Big Data Analytics. In this paper, we have reviewed the available literature on Big Data Analytics using Quantum Computing for Machine Learning and its current state of the art. We categorized the Quantum Machine learning in different subfields depending upon the logic of their learning followed by a review in each technique. Quantum Walks used to construct Quantum Artificial Neural Networks, which exponentially speed-up the quantum machine learning algorithm is discussed. Quantum Supervised and Unsupervised machine learning and its benefits are compared with that of Classical counterpart. The limitations of some of the existing Machine learning techniques and tools are enunciated, and the significance of Quantum computing in Big Data Analytics is incorporated. Being in its infancy as a totally new field, Quantum computing comes up with a lot of open challenges as well. The challenges, promises, future directions and techniques of the Quantum Computing in Machine Learning are also highlighted.
Quantum Machine Learning (QML) is a new discipline that unites artificial intelligence and quantum computing and can address computational problems of big data analysis. Traditional machine learning algorithms may be pushed to their limits in dealing with the increased complexity and scale of today's data sets and thus are unable to find useful insights within a reasonable time frame. Quantum computing, capable of tapping quantum mechanical processes like superposition and entanglement, is capable of turning this field upside down. In this paper, the concepts behind quantum computing are discussed and how machine learning could be used using the assistance of quantum algorithms in order to better deal with big data. It explains the most optimal quantum algorithms like Quantum Support Vector Machines (QSVM), Quantum Principal Component Analysis (QPCA), and Quantum k-Means Clustering, and why they are better and faster compared to their classical counterparts. It also explores actual applications in medicine, finance, and artificial intelligence. It also addresses the limits and disadvantages of existing quantum technology like hardware limitations, noise, and complexity of algorithms. Last but not least, it also considers the future direction of trends within the field, with emphasis placed on hybrid quantum-classical systems and quantum machine learning application within the construction of big data analysis.
The product development, process optimization and chemical discovery are also among the actual manufacturing areas for witnessing the major innovations for quantum computing. The quantum simulations can also be capable of replacing the laboratory experiments that reduces the research cost and also even minimize the requirement of animal and human testing. Apart from that, quantum computers can bring the huge potential advantages within the financial sectors that are from deeper analytics to the latest and faster trading possibilities. In the current scenario, the existing computers struggle in calculating big data and are unable to solve several complex problems. Therefore, these problems are much more expected to be resolved within a few seconds by the help and power of the quantum computers. Apart from that, it is also predicted that the AI applications within the particular ML can also benefit from the advanced technology of quantum computing. Keyword :Machine learning, Artificial Intelligence, Quantum computing, Cyber security.
Quantum Computing can be defined as an intersection among Quantum Physics, Computer Science and Mathematics. Though the behavior of quantum mechanics is spooky, the mathematics behind it is simpler and easier than classical one. Using superposition and entanglement, quantum computing can solve complex problems efficiently and it would take less amount of time than its classical counterpart to do so. Machine Learning (ML) is a subset of Artificial Intelligence (AI) which helps to learn from data, analyze patterns and make itself improved. When quantum computing is blended with Machine Learning, Quantum Machine Learning pops up. In this survey, we explore what Quantum Machine Learning (QML) is and how it actually works. We try to demonstrate the very quantum touch on our known machine learning algorithms. We also delve into the various application areas of QML. This paper helps to understand QML from basics to advanced levels.
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.