Industrial chemists are already exploring ways to integrate quantum methods into their work. We do not have a good way to create these computational spaces with classical computers, which limits their usefulness without quantum computation. This turns out to be a much more efficient way of solving complex problems like chemical simulations. Quantum algorithms take a new approach to these sorts of complex problems - creating multidimensional computational spaces. No computer has the working memory to handle all the possible permutations of molecular behavior using any known methods. But as it moves past the simplest, most straightforward molecules available, the supercomputer stalls. This is an expensive, time-consuming process that impedes progress in fields as diverse as medicine and semiconductor design.Ī classical supercomputer might try to simulate molecular behavior with brute force, leveraging its many processors to explore every possible way every part of the molecule might behave. If they want to know how a slight tweak would impact its behavior, they usually need to synthesize the new version and run their experiment all over again. Today, for the most part, if scientists want to know how a molecule will behave they have to synthesize it and experiment with it in the real world. But it will struggle to solve more complex problems, like simulating how those molecules behave. Let's look a an example that shows how quantum computers can succeed where classical computers fail:Ī classical computer might be great at difficult tasks like sorting through a big database of molecules. Computers that make calculations using the quantum states of quantum bits should in many situations be our best tools for understanding it. There are some complex problems that we do not know how to solve with classical computers at any scale. Identifying subtle patterns of fraud in financial transactions or new physics in a supercollider are also complex problems. Modeling the behavior of individual atoms in a molecule is a complex problem, because of all the different electrons interacting with one another. When classical computers fail, it's often due to complexity.Ĭomplex problems are problems with lots of variables interacting in complicated ways. 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. They struggle to solve certain kinds of problems. However, even supercomputers are binary code-based machines reliant on 20th-century transistor technology. These are very large classical computers, often with thousands of classical CPU and GPU cores capable of running very large calculations and advanced artificial intelligence. When scientists and engineers encounter difficult problems, they turn to supercomputers.
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