The list of tasks where quantum computers have advantage is growing

CONNIE ZHOU/IBM

Quantum computers are pushing further into the world of the impossible. Three new examples of calculations that no other type of machine can do show how quantum computers are becoming useful as scientific tools.

There are many situations in science where solving a problem requires extensive computer calculations, such as when trying to predict the behaviour of large molecules. But in some cases, the calculations are so complex, that traditional computers will never reach an answer.

This is the point where quantum computers can have “quantum advantage” as they can use quantum phenomena to produce a solution. But how can you be sure that an answer that only one machine can produce can be trusted?

Researchers at IBM and colleagues have now claimed three new cases of quantum advantage, together with arguments for why these calculations should be trusted.

Two of the cases were simulations of mathematical models that are of particular interest to physicists who study materials.

In one, the model aims to capture how quantum materials behave when hit by laser light, focusing on a type of magnet. Using a quantum computer, the researchers discovered that some of the magnet’s properties oscillate, which was not known before. Notably, simulations on the Fugaku supercomputer in Japan as well as on an NVIDIA GPU failed to produce results.

In the second case, the team focused on how information gets scrambled within a very uneven material, which could help researchers to better understand the chaotic processes that take place, for example, when a catalyst is used to accelerate a chemical reaction.

Here, researchers compared the quantum computer’s output to a leading simulation method for traditional computers – and one that has dispelled quantum advantage claims in the past. For some simulation parameters, these methods could not measure up to the quantum computer.

For both simulations, the team made an effort to estimate how much the quantum computer could be trusted by repeating the simulation on several different quantum computers, each of which had slightly different amounts of noise, and they also purposefully injected noise into some computational runs. The comparison between these differently noisy computations enabled the team to quantify how much of the quantum computer’s output can be trusted and how much is just noise, says Abhinav Kandala at IBM.

Finally, the third example focused on a sampling problem that looks at the output of many quantum computing circuits that have pre-specified properties, such as the distribution of mathematical operations within them. Researchers already knew that sampling problems are difficult for conventional computers, but even though a quantum computer could output an answer they previously struggled to verify how meaningful it was – whether it was mired by errors. The team devised a procedure that also involved tweaking then repeating the computation to quantify the fidelity of the quantum computer’s answer.

Quantum computers are now operating like we’ve always wanted them to, says Kandala. “I hope people begin to use them as scientific instruments that have trusted outcomes.”

These new efforts to verify just how well a quantum is doing is a step in the right direction, says Dominik Hangleiter at ETH Zurich in Switzerland, who wasn’t part of the team. “You can actually take the data from the experiment and get an estimate for how good the experimental runs were,” he says. This process is still not ideal as it is not fully agnostic of the quantum computer’s specifics, but it is necessary for the still fairly small and noisy quantum computers that we have today, says Hangleiter.

In addition to further improvements on the verification front, quantum computer simulations of materials also need to be pushed further. Emanuele Dalla Torre at Bar-Illan University in Israel says that the magnet simulation made findings significant from a fundamental-science perspective yet it was still idealised compared to real-world magnets. “The problem itself is, at the end, a basic science problem, which doesn’t have necessarily a clear usage right now,” he says.

But team is optimistic about how these new developments could jumpstart an era of quantum usefulness. “We are creating the toolbox for how we can trust the output [of quantum computers]. And once we can trust the output, then we can start to nail down applications,” says Jay Gambetta at IBM.