QUANTUM COMPUTING: A REVIEW ON BIG DATA ANALYTICS AND DATA SECURITY

Abstract

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.