The system could handle massive digital datasets and make these virtually impossible problems solvable.
Humans are continuously generating larger sets of data. What if your dataset gets too large or complex that your traditional data processing applications could not handle it?
A team of researchers from MIT, University of Waterloo and University of Southern California are working on a new approach that will use quantum computers for dealing with the ever-growing influx of information. Their futuristic approach is based on a strange branch of mathematics called algebraic topology which is concerned with properties that remain the same under continuous bending and stretching in every possible manner.
Such system is especially useful for processing complex data such as internal wiring of the brain, the electric power grid and global interconnections of internet. This approach will help reduce distortions which are faced when useful information is extracted from large data sets.
In topological systems, the basic features of data never change no matter how much they are stretched, suppressed or deformed.
Seth Lloyd, lead author of the study explains it as if a dataset has 300 points, then it will require “a computer the size of the universe” to analyze its features through conventional approach. It would take 2300 (two to the 300th power) processing units which is almost equal to all of the particles in the universe. In other words, it is impossible to process that amount of data.
Solving the same problem with new system using a quantum computer would require just 300 quantum bits and it will possibly take few years to materialize this concept. The only drawback right now is that it is computationally very expensive.
“Our algorithm shows that you don’t need a big quantum computer to kick some serious topological butt,” said Lloyd. “By applying topological analysis to datasets gleaned by electroencephalography or functional MRI, you can reveal the complex connectivity and topology of the sequences of firing neurons that underlie our thought processes.”
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“You could apply it to the world’s economy, or to social networks or almost any system that involves long-range transport of goods or information.”