The first batch of commercial quantum computers are close to being released now. According to both Google and IBM, we’ll see these systems in place across the globe within the next five years at most, but IBM is aiming for just two. However, before that happens, it’s likely to be chemists that will benefit first from the likes of quantum computing in areas such as improving batteries or electronics.
It’s been a long time coming, but the world of quantum computing is very close now. Quantum computers will take computing to the next level as it will allow us to carry out calculations that a conventional computer could never deal with. We’ve seen big investments coming from some big companies over the past few years too as we get ready for quantum computing and that includes IBM, Google, Microsoft, and Intel.
“From the point of view of what is theoretically proven, chemistry is ahead. We have more confidence in the smaller systems for chemistry,” says Scott Crowder, a chief technology officer for IBM. However, payoffs have been limited in the past as even the most powerful supercomputers have been unable to mimic that of quantum behaviors, even in small molecules. Alan Aspuru-Guzik is a chemistry professor at Harvard, and he says, “Right now we have to calibrate constantly with experimental data. Some of that will go away if we have a quantum computer.”
In the race to get a commercial computer out there, Microsoft is going to a less mature form than its main competitors IBM and Google yet they’ve been seen to making the most effort in this area. In particular, the company’s been focusing on chemistry and materials science while demonstrating how a hybrid conventional/quantum computer system could work. “It has great promise for studying molecules,” says Krysta Svore, leader of Microsoft’s quantum algorithm team. Chemistry may be the first area to apply quantum computing to as this is the area researchers best understand where to apply it. But, other areas are being explored in which quantum computing could be applied most effectively, including machine learning and encryption.
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