Why do We Need Quantum Computing
Supercomputers aren’t all that super for some issues.
Supercomputers are used by scientists and engineers when they are faced with challenging tasks. These are enormous classical computers that frequently have thousands of cores for both the GPU and CPU. However, some types of issues are difficult for even supercomputers to solve.
When a supercomputer struggles, it’s often because the large classical machine was given a challenging problem to answer. Complexity is commonly to blame for the failure of traditional computers.
Multiple variables that interact in intricate ways are considered complex problems. Because there are so many different electrons interacting with one another, modelling the behaviour of individual atoms in a molecule is a challenging task. It is difficult to determine the best paths for a few hundred tankers in a vast transportation network.
Why Quantum Computer Are Fast
Let’s look at an illustration of how quantum computers can outperform traditional computers:
When it comes to challenging jobs like sorting through a vast library of protein sequences, a supercomputer may excel. However, it will find it difficult to spot the minute patterns in that data that govern how those proteins act.
Long chains of amino acids called proteins fold into intricate forms to form vital biological machinery. Understanding protein folding is a challenge with significant biological and medical ramifications.
A traditional supercomputer would use brute force to attempt protein folding, using its numerous processors to examine every potential configuration of the chemical chain before coming to a conclusion. The supercomputer, however, stops as the protein sequences grow longer and more complicated. Theoretically, a chain of 100 amino acids may fold in one of many trillions of different ways. No computer has enough working capacity to store all the different ways that individual folds could be combined.
In order to solve these kinds of difficult problems, quantum algorithms create multidimensional spaces where the relationships between the many data points begin to take shape. That pattern might be the combination of folds that requires the least amount of energy to create in the case of a protein folding issue. The problem’s solution is that arrangement of folds.
Because traditional computers cannot build these computational spaces, they are unable to detect these patterns. Early quantum algorithms are now available to uncover protein folding patterns in wholly novel, more effective methods without the time-consuming verification steps of classical computers. These algorithms may be able to solve protein folding issues that are beyond difficult for any supercomputer when quantum hardware scales and develops..
