Why do we need quantum computers?
For some problems, supercomputers aren’t that super.
When scientists and engineers encounter difficult problems, they turn to supercomputers. These are very large classical computers, often with thousands of classical CPU and GPU cores. However, even supercomputers struggle to solve certain kinds of problems.
If a supercomputer gets stumped, that's probably because the big classical machine was asked to solve a problem with a high degree of complexity. When classical computers fail, it's often due to complexity.
Complex problems are problems with lots of variables interacting in complicated ways. Modeling the behavior of individual atoms in a molecule is a complex problem, because of all the different electrons interacting with one another. Sorting out the ideal routes for a few hundred tankers in a global shipping network is complex.
Why quantum computers are faster
Let's look that shows how quantum computers can succeed where classical computers fail:
A supercomputer might be great at difficult tasks like sorting through a big database of protein sequences. But it will struggle to see the subtle patterns in that data that determine how those proteins behave.
Proteins are long strings of amino acids that become useful biological machines when they fold into complex shapes. Figuring out how proteins will fold is a problem with important implications for biology and medicine.
A classical supercomputer might try to fold a protein with brute force, leveraging its many processors to check every possible way of bending the chemical chain before arriving at an answer. But as the protein sequences get longer and more complex, the supercomputer stalls. A chain of 100 amino acids could theoretically fold in any one of many trillions of ways. No computer has the working memory to handle all the possible combinations of individual folds.
Quantum algorithms take a new approach to these sorts of complex problems -- creating multidimensional spaces where the patterns linking individual data points emerge. In the case of a protein folding problem, that pattern might be the combination of folds requiring the least energy to produce. That combination of folds is the solution to the problem.
Classical computers can not create these computational spaces, so they can not find these patterns. In the case of proteins, there are already early quantum algorithms that can find folding patterns in entirely new, more efficient ways, without the laborious checking procedures of classical computers. As quantum hardware scales and these algorithms advance, they could tackle protein folding problems too complex for any supercomputer.
Proteins are long strings of amino acids that become useful biological machines when they fold into complex shapes. Figuring out how proteins will fold is a problem with important implications for biology and medicine.
A classical supercomputer might try to fold a protein with brute force, leveraging its many processors to check every possible way of bending the chemical chain before arriving at an answer. But as the protein sequences get longer and more complex, the supercomputer stalls. A chain of 100 amino acids could theoretically fold in any one of many trillions of ways.
Quantum computing is a rapidly-emerging technology that harnesses the laws of quantum mechanics to solve problems too complex for classical computers.
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