Quantum computers today are smaller in computational scope – the chip inside your smartphone contains billions of transistors whereas the most powerful quantum computers contain a few hundred quantum equivalents of transistors. They are also incredible. If you run the same calculations over and over, chances are they will give different answers each time.
But with their intrinsic ability to consider many possibilities at once, quantum computers don’t have to be huge to tackle some of the trickiest problems of computation, and on Wednesday, IBM researchers announced that they managed the unreliability. developed a method to do this that will lead to reliable, useful answers.
“What IBM has shown here is a surprisingly important step toward making progress toward really serious quantum algorithm design,” said Dorit Aharonov, professor of computer science at the Hebrew University of Jerusalem.
While in 2019 Google researchers claimed that they “Quantum Supremacy” was achieved – A task on a quantum computer is performed much more quickly than on a conventional computer – IBM researchers say they have achieved something new and more useful, albeit more modestly named.
“We are entering this phase of quantum computing, which I call utility,” said Jay Gambetta, vice president of IBM Quantum. “The Age of Utility.”
A team of IBM scientists who work for Dr. Gambetta Their results are described in a paper published Wednesday in the journal Nature.,
Present-day computers are called digital, or classical, because they deal with bits of information that are 1 or 0, on or off. A quantum computer computes on quantum bits, or qubits, which capture a more complex state of information. As a thought experiment by physicist Erwin Schrödinger postulated that a cat could be in a quantum state that is both dead and alive, a cubit could be both 1 and 0 at the same time.
This allows quantum computers to perform multiple calculations in one pass, whereas digital computers have to perform each calculation separately. By speeding up computation, quantum computers could potentially solve large, complex problems in fields such as chemistry and materials science that are out of reach today. Quantum computers may also have a darker side by threatening privacy through algorithms that break the security used for passwords and encrypted communications.
When Google researchers claimed supremacy in 2019, they said their quantum computer performed a calculation in 3 minutes 20 seconds that would take about 10,000 years on a state-of-the-art conventional supercomputer.
But some other researchers, including those from IBM, rejected the claim, saying the problem was artificial. “Google’s experiment, as impressive as it was, and it was really impressive, is doing something that isn’t interesting for any application,” said Dr. Aharonov, who serves as chief strategy officer at quantum computing company Qedma. also work.
Google’s computations also turned out to be less impressive than they first appeared. A team of Chinese researchers was able to demonstrate Same calculation on a non-quantum supercomputer in just five minutesMuch faster than the 10,000 years the Google team estimated.
In the new study, the IBM researchers took on a different task that interested physicists. They used a quantum processor with 127 qubits to simulate the behavior of 127 atom-scale bar magnets – small enough to be governed by the scary laws of quantum mechanics – in a magnetic field. This is a simplified system known as the Ising model, which is often used to study magnetism.
The problem is so complex that even the biggest, fastest supercomputers can’t calculate the exact answer.
On a quantum computer, a calculation takes less than a thousandth of a second to complete. Each quantum calculation was unreliable—the fluctuations of quantum noise inevitably intruded and induced errors—but each calculation was quick, so it could be performed repeatedly.
Indeed, for many calculations, additional noise was intentionally added, making the answers even more unreliable. But by varying the amount of noise, the researchers can tease out specific characteristics of the noise and its effects at each stage of the computation.
“We can amplify the noise very precisely, and then we can run the 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 look at what results are there 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 bypass it by devising very clever ways to reduce the noise,” Dr Aharonov said. “And that’s what they do.”
In total, the computer made 600,000 calculations, converging on the answer for the overall magnetization produced by the magnets 127 times.
But how good was the answer?
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 in a conventional computer, classical algorithms can generate approximate answers, how compression in JPEG images reduces important data to a minimum. technology is similar. File size while preserving most of the image details.
Michael Zatel, 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 I expected,” Dr. Zalatel said.
Some configurations of the Ising model can be solved exactly, and both classical and quantum algorithms agree on simple examples. For more complex but solvable examples, the quantum and classical algorithms gave different answers, and it was the quantum one that was correct.
Thus, for other cases where quantum and classical calculations differ and no exact solution is known, “there is reason to believe that the quantum result is more accurate,” said Sajanth Anand, a graduate student at Berkeley who A lot of work has been done on classical conjecture.
It is not clear that quantum computing is unquestionably the winner over classical techniques for the Ising model.
Mr Anand is currently trying to implement a version of error mitigation for the classical algorithm, and it is possible that it could match or exceed the performance of quantum computations.
“It is not clear that they have achieved quantum supremacy here,” Dr. Zalatel said.
In the long run, quantum scientists hope that a different approach, error correction, will be able to detect and correct calculation mistakes, and this will open the door for quantum computers to advance to many uses.
Error correction is already used in traditional computers and data transmission to correct errors. But for quantum computers, error correction is likely years away, requiring better processors capable of processing many more qubits.
Error mitigation, the IBM scientists believe, is an interim solution that can now be used for increasingly complex problems beyond the Ising model.
“This is one of the simplest natural science problems that exist,” Dr. Gambetta said. “So that’s a good one to start with. But now the question is how do you generalize that and move on to more interesting natural science problems?”
They may include exploring the properties of exotic materials, accelerating drug discovery, and modeling fusion reactions.