Quantum computers promise to speed up the processing and integration of our massive data sets, enhancing and transforming our machine learning and artificial intelligence capabilities.
Every day, we generate 2.5 exabytes of data. This is the equivalent of 250,000 Library of Congress libraries or the data of 5 million laptops. Every minute of every day, 3.2 billion internet users across the world continue to feed the data banks with 9,722 Pinterest pins, 347,222 tweets, 4.2 million Facebook likes, and ALL the other data we generate by shooting pictures and videos, storing documents, creating accounts, and more.
We’ve reached the limitations of traditional computers’ data processing capability, yet the data keeps expanding. While Moore’s Law, which predicts that the number of transistors on integrated circuits would double every two years, has held up admirably since its inception in 1965, those transistors are currently as tiny as we can get them with current technology. That’s why the industry’s major players are racing to be the first to develop a functional quantum computer that would be tenfold more powerful than today’s computers and capable of processing all of the data we create every day and solving more complicated issues.
Quantum computers are capable of quickly solving complex problems
It’s likely that once one of these industry leaders succeeds in developing a commercially viable quantum computer, these quantum computers would be able to do computations in seconds that would take today’s computers thousands of years to complete. Google claims to have a quantum computer that is 100 million times faster than any current device. That will be important if we are to handle the massive amounts of data we create and solve extremely difficult issues. Translating our real-world challenges into quantum language is crucial to our success.
Our data sets are expanding in complexity and size faster than our processing capabilities, putting a strain on our computing infrastructure. While today’s computers struggle or are unable to tackle particular issues, quantum computing’s capability is projected to solve these same difficulties in seconds. It’s expected that developments in quantum computing technology would help artificial intelligence, particularly machine learning, and that this will continue even before a comprehensive quantum computing solution is ready. Quantum computing methods allow us to improve on what machine learning can currently do.
Solutions Will Be Optimised by Quantum Computers
Another way quantum computing can help usher in a revolution is by allowing us to sample data and optimize all types of challenges we face, from portfolio analysis to the best delivery routes, and even determining what the best treatment and pharmaceutical routine is for each individual.
With the rise of big data, we’ve had to adapt our computer architecture, necessitating the need for a new computational technique to dealing with large data. Not only does it have a broader reach, but the challenges we’re attempting to tackle are also somewhat different.
Quantum computers are more capable of solving sequential tasks quickly. The ability they enable businesses and even consumers to make better decisions might be precisely what it takes to persuade enterprises to invest in new technology when it becomes available.
Patterns in Large Data Sets Could Be Spotted by Quantum Computers
Quantum computing is projected to be capable of swiftly searching very huge, unsorted data sets for patterns or abnormalities. Quantum computers may be able to access all things in your database at the same time, allowing them to find these commonalities in seconds. While this is technically conceivable today, it requires a parallel computer to look at each record one by one, which takes an inordinate amount of time and may never happen depending on the size of the data collection.
Data Integration from a Variety of Sources
Furthermore, due to the integration of extremely disparate data sets, substantial advancements are envisaged when quantum computers become accessible. Although this may be challenging at initially without human participation, human input will aid the computers in learning how to integrate data in the future. So, if a research team wishes to compare raw data sources with various schemas (terminology and column headings), a computer would have to first comprehend the link between the schemas before the data could be compared. Breakthroughs in the understanding of the semantics of natural language, one of the most difficult tasks in artificial intelligence, are required to achieve this. Humans, on the other hand, may provide input that trains the system for the future.
