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Let's look at example 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.

How complexity stumps supercomputers

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 computers are built for complexityQuantum algorithms take a new approach to these sorts of complex problems -- creating multidimensional spaces where the patterns linking individual data points emerge. 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.

See the rest here:
What is Quantum Computing? | IBM