The Future of Big Data Processing: Quantum Computing

By: Jai Prakash Verma, Jayneel Shah and Smiti Kothari

We live in a world where we generate an unprecedented amount of data each day. To put it into perspective, as a society, we currently produce 2.5 exabytes of data every 24 hours, which is equivalent to one billion gigabytes. That is like streaming 1.25 million HD movies or storing 40 million photos every minute! With over 4.8 billion internet users worldwide, we generate data through our social media activity, online shopping, GPS tracking, and many more. However, the real challenge is in deriving meaningful insights from this data to make informed business decisions. In the last decade, there has been an explosive growth in the amount of data generated by individuals, organizations, and machines. This explosion of data, commonly referred to as “big data,” has given rise to the field of big data analytics, which requires the use of advanced computing techniques to extract valuable insights and knowledge from massive datasets. Big data analytics has become a need with the advent of linked devices, social media platforms, and the internet of things.

The impact of the big data trend is clear as it has had a profound influence on various research directions, including geospatial sciences and GIS. The integration of big data with GIS has brought about significant implications for how spatial information is acquired and utilized, resulting in a substantial increase in the volume, velocity, and variety of data. Consequently, a challenge arises with handling this large amount of data, as traditional methods of processing and analysis are no longer sufficient. Some of the applications for geospatial data and GIS are they can be used to optimize crop yields, manage soil health and monitor weather patterns. They can be also used to monitor and manage resources and track land use changes.

By incorporating big data analytics, organizations now have the ability to make use of this vast pool of geospatial data to uncover insights that were previously unattainable. The combination of GIS and big data opens up a wide range of possibilities, and it is fascinating to witness the growing interest and the diverse ways in which organizations are harnessing this powerful amalgamation.

As companies generate ever-increasing volumes of data, traditional computing systems are finding it challenging to meet the demand for faster and more efficient processing. However, there’s hope on the horizon in the form of quantum computing. This groundbreaking technology offers exponential computing power and lightning-fast processing speeds compared to classical computers. It holds immense promise in overcoming the limitations we face. As data grows in both quantity and complexity, the advancement of quantum computing becomes crucial for unlocking the true potential of big data analytics. Many experts view quantum computing as the future of computing itself, and its integration with big data analytics has the potential to revolutionize how organizations handle and make use of data. The possibilities are truly exciting!

A fundamentally different methodology than classical computers, quantum computing processes data using quantum bits (qubits) as opposed to binary bits. Qubits, which differ from binary bits in that they may exist in more than one state at once, allow for intricate concurrent computations that are not possible with traditional computers. Quantum computing is a potential solution for big data analytics since it has the capacity to handle enormous data sets more quickly and provide data to AI technologies and applications for in-depth analysis of patterns and anomalies. Quantum computing may also help with data integration by quickly comparing two datasets’ schemas to determine their link. Quantum computing is far more effective than conventional computer techniques because of its enormous parallel processing capabilities using MapReduce which enables the extraction of important insights from large and complex information. Quantum computing is a viable future option for managing massive data since it enables improved analytics and quicker decision-making for organizations.
In conclusion, the integration of big data with GIS and the development of quantum computing has the potential to revolutionize the processing, analysis, and application of geographic data in organizations. The growth of big data has posed a challenge to conventional data processing and analysis techniques, but thanks to the union of big data with GIS, organizations may now leverage spatial data to acquire meaningful insights. Quantum computing is about addressing the limitations of traditional computing, and it also has the potential to greatly increase the power and effectiveness of data processing. The confluence of these technologies is opening up new opportunities for organizations across all industries by providing new routes to more sophisticated analytics, quicker decision-making, and better outcomes. Fast innovation and a persistently high level of interest in the combination of big data with GIS and quantum computing will undoubtedly characterize future advancements.

The reason behind writing this blog was to discuss how the explosive growth of big data has created a need for advanced computing technologies to extract insights from massive datasets. For processing and analyzing massive data more fast and effectively than traditional computing, quantum computing presents a viable alternative. Organizations now have unprecedented options to get advanced insights, make quicker choices, and produce better results because of the convergence of GIS, big data analytics, and quantum computing. In the upcoming blogs, we will be talking about several quantum computing systems, potential applications for quantum computing, and its infrastructure.