Quantum computing breakthrough ushers in a new era, says IBM

Today’s quantum computers have a small computational scope: the chip inside your smartphone contains billions of transistors, while the most powerful quantum computer contains a few hundred of the quantum equivalent of a transistor. They are also unreliable. If you run the same calculation over and over again, it will most likely produce different answers each time.

But with their intrinsic ability to consider many possibilities at once, quantum computers don’t have to be very large to tackle some thorny computing problems, and on Wednesday IBM researchers announced they had devised a method to handle unreliability. in a way that would lead to reliable and useful answers.

“What IBM showed here is really an incredibly important step in that direction of moving toward serious quantum algorithmic design,” said Dorit Aharonov, a professor of computer science at the Hebrew University of Jerusalem who was not involved in the research.

While Google researchers in 2019 claimed that had achieved “quantum supremacy” – a task that is accomplished much faster on a quantum computer than on a conventional one – IBM researchers say they have come up with something new and more useful, albeit with a more modest name.

“We are entering this phase of quantum computing that I call utility,” said Jay Gambetta, vice president of IBM Quantum. “The Age of Utility”.

A team of IBM scientists working for Dr. Gambetta described their results in a paper published Wednesday in the journal Nature..

Today’s computers are called digital or classical because they handle bits of information that are 1 or 0, on or off. A quantum computer performs calculations on quantum bits, or qubits, which capture a more complex state of information. Just as a thought experiment by physicist Erwin Schrödinger postulated that a cat could be in a quantum state both alive and dead, a qubit can be 1 and 0 simultaneously.

That allows quantum computers to do many calculations in a single pass, while digital ones have to perform each calculation separately. By speeding up computing, quantum computers could potentially solve large, complex problems in fields like chemistry and materials science that are currently out of reach. Quantum computers could also have a darker side, threatening privacy through algorithms that break the protections used for passwords and encrypted communications.

When Google researchers made their claim to supremacy in 2019, they said their quantum computer performed a calculation in 3 minutes and 20 seconds that would take around 10,000 years on a conventional, next-generation supercomputer.

But some other researchers, including those at IBM, dismissed the claim, saying the problem was contrived. “Google’s experiment, as impressive as it was, and really impressive, is doing something that is not interesting for any application,” said Dr. Aharonov, who also works as chief strategy officer at Qedma, a quantum computing company.

Google’s calculation also turned out to be less impressive than it first appeared. A team of Chinese researchers was able to perform the same calculation on a non-quantum supercomputer in just over five minutesmuch faster than the 10,000 years that the Google team had estimated.

The IBM researchers in the new study performed a different task, one that interests physicists. They used a quantum processor with 127 qubits to simulate the behavior of 127 atomic-scale bar magnets, small enough to play by the hair-raising rules of quantum mechanics, in a magnetic field. That’s a simple system known as the Ising model, which is often used to study magnetism.

This problem is too complex to compute a precise answer on even the largest and fastest supercomputers.

On the quantum computer, the calculation took less than a thousandth of a second to complete. Every quantum computation was unreliable (quantum noise fluctuations inevitably intrude and induce errors), but every computation was fast, so it could be done repeatedly.

In fact, for many of the calculations, extra noise was deliberately added, making the answers even less reliable. But by varying the amount of noise, the researchers were able to discover the specific characteristics of the noise and its effects at each step of the calculation.

“We can amplify the noise very precisely and then we can run that same circuit again,” said Abhinav Kandala, manager of quantum capabilities and demonstrations at IBM Quantum and author of the Nature paper. “And once we have the results for these different noise levels, we can extrapolate to what the result would have been in the absence of noise.”

In essence, the researchers were able to subtract the effects of noise from unreliable quantum calculations, a process they call error mitigation.

“You have to avoid that by coming up with very clever ways to mitigate the noise,” Dr. Aharonov said. “And this is what they do.”

In all, the computer ran the calculation 600,000 times, converging on an answer for the total magnetization produced by the 127 bar magnets.

But how good was the response?

For help, the IBM team turned to physicists at the University of California, Berkeley. Although an Ising model with 127 bar magnets is too large, with too many possible configurations, to fit on a conventional computer, classical algorithms can produce approximate answers, a technique similar to how compression in JPEG images discards less crucial data to reduce file size while preserving most of the image detail.

Michael Zaletel, a professor of physics at Berkeley and author of the Nature paper, said that when he started working with IBM, he thought their classical algorithms would work better than quantum ones.

“It turned out a little different than what I expected,” Dr. Zaletel said.

Certain configurations of the Ising model can be exactly solved, and both the classical and quantum algorithms agreed in the simplest examples. For more complex but solvable instances, the quantum and classical algorithms produced different answers, and the quantum was the correct one.

Thus, for other cases where the quantum and classical calculations diverged and exact solutions are not known, “there is reason to believe that the quantum result is more accurate,” said Sajant Anand, a Berkeley graduate student who did much of the research. of work on classical approaches.

It is not clear that quantum computing is the outright winner over classical techniques for the Ising model.

Mr. Anand is currently trying to implement a bug-mitigating version of the classical algorithm, and it is possible that it will match or exceed the performance of quantum computations.

“It is not obvious that they have achieved quantum supremacy here,” Dr. Zaletel said.

In the long term, quantum scientists hope that a different approach, error correction, will be able to detect and correct miscalculations, and that will open the door for rapidly advancing quantum computers for many uses.

Error correction is already used in conventional computers and data transmission to correct distortions. But for quantum computers, error correction is likely years away, requiring better processors capable of processing many more qubits.

IBM scientists believe that error mitigation is a workaround that can now be used for increasingly complex problems beyond the Ising model.

“This is one of the simplest natural science problems out there,” said Dr. Gambetta. “So it’s good to start. But now the question is, how do you generalize and tackle more interesting natural science problems?

These could include discovering the properties of exotic materials, accelerating drug discovery, and modeling fusion reactions.