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.
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.
Big data is a hot buzzword that has impacted every field nowadays. Big data applies to applications that are beyond storage, processing and analyzing capabilities of traditional processes and tools. Big data is the gold mine of data piled up and to mine it for useful insights, brands it both opportunistic and challenging at one go. The healthcare sector is the best example of big data, where data is not only produced in huge quantities but is very complex and diverse because of the varied formats of the data. In such a scenario, quantum computing offers promises of rescue by providing new features in information processing systems, particularly in big data analytics. In this study, we have proposed a framework of how the power of quantum computing can be used for solving the big data problem particularly focusing it on healthcare data sector. A brief review of quantum computing techniques is also carried in the form of literature review. The challenges, promises, future directions, and techniques of quantum computing in big data analytics are also highlighted.
This study provides a comprehensive review of the advancements, challenges, and potential implications of quantum computing in the field of big data analytics. The primary objective is to assess how quantum computing paradigms are transforming data processing and analysis, with a focus on their application across various sectors, including healthcare, finance, and scientific research. Employing a systematic literature review and content analysis, the study analyzes peer-reviewed articles, conference proceedings, and academic journals from databases such as PubMed, IEEE Xplore, and ScienceDirect. Key findings reveal that quantum computing, with its advanced algorithms and machine learning techniques, offers significant improvements in computational speed and efficiency over classical computing methods. This technological advancement enables the handling of large and complex datasets, presenting new opportunities in data analytics. However, the study also identifies challenges such as scalability, error correction, and integration with existing systems, which currently limit the full potential of quantum computing in big data analytics. The study concludes with strategic recommendations for industry leaders and policymakers, emphasizing the need for investment in research and development, the establishment of regulatory frameworks, and the development of educational programs to support this emerging field. Future research directions are suggested, focusing on overcoming technological limitations and exploring the long-term implications of quantum computing in various industries. This study contributes valuable insights into the evolving landscape of quantum computing and its significant impact on big data analytics. Keywords: Quantum Computing, Big Data Analytics, Advanced Algorithms, Data Processing.
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 that refers to data that exceeds the storage capacity and processing capability of traditional computers, and gaining insight from vast amounts of data is a significant task. Quantum computing saves the day by making numerous promises in information processing systems, including big data insights. Quantum computers are machines that employ quantum physics to store and process data. This could be quite useful for particular applications, where they can significantly outperform even our most powerful supercomputers. In typical computers such as laptops and smartphones, data is stored in binary “bits,” which can be either 0s or 1s. A quantum bit, or qubit, is the basic memory unit of a quantum computer. Regardless of the fact that quantum computing is a relatively new technology, it is already being employed in data analytics. This chapter explains the review of quantum computing and big data, with challenges, analytics, and future.
The world's most difficult problem during the preceding decade was the big data problem. The big data challenge refers to the fact that data is growing at a much faster rate than computational rates. People, as well as virtually all organizations and scientific institutions, are keeping a rising amount of data as the cost of storage falls every day. Large amounts of data are generated by social activities, scientific inquiries, biological discoveries, and sensor devices. Big data is beneficial to society and economy, but it also poses challenges to scientific communities. Traditional tools, machine learning algorithms, and methodologies cannot handle, manage, or analyze large amounts of data. Quantum computing, in the realm of Big Data, allows businesses to collect and analyze large volumes of data quickly using quantum algorithms. Separate data sets may be detected, analyzed, integrated, and diagnosed with far greater ease. To find patterns, all of the items of a large database can be studied at the same time. As a result, it may be years before quantum computing makes its way into most businesses or becomes a common data analytics tool. Quantum computing will still be a relatively new technology in 2021. Machine learning algorithms are currently improving thanks to breakthroughs in quantum computing technology. There's still a lot to learn about quantum computing's capabilities and the implications of such a strong technology.
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.