Quantum computers today are small in computational size – the chip in 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 do the same calculation over and over, they will most likely produce different answers each time.
But with their inherent ability to consider many possibilities at once, quantum computers don’t need to be very large to tackle certain thorny computational problems. would lead to reliable, useful answers.
“What IBM showed here is really an amazingly important step in that direction to make progress 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 project. research.
While researchers at Google claimed in 2019 that they had achieved “quantum supremacy” — a task performed much faster on a quantum computer than on a conventional computer — IBM researchers say they’ve achieved something new and more useful, albeit with a more humble name.
“We’re 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, detailed their results in a paper published Wednesday in the journal Nature.
Contemporary computers are called digital or classic because they deal with bits of information that are 1 or 0, on or off. A quantum computer performs calculations on quantum bits, or qubits, that 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 that is both dead and alive, a qubit can be both 1 and 0 at the same time.
As a result, quantum computers can make many calculations at once, while digital computers have to perform each calculation separately. By speeding up computation, quantum computers could potentially solve large, complex problems in fields like chemistry and materials science that are out of reach today. Quantum computers can also have a dark side, threatening privacy through algorithms that break the protection of passwords and encrypted communications.
When Google researchers claimed supremacy in 2019, they said their quantum computer performed a calculation in 3 minutes and 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, ignored the claim and said the problem was fabricated. “Google’s experiment, as impressive as it was, and it was really impressive, does something that is of no interest to any application,” said Dr. Aharonov, who also serves as Chief Scientific Officer of Qedma, a quantum computing company.
The Google 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 minutes, much faster than the 10,000 years estimated by the Google team.
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 be governed by the ghostly 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 for even the largest, fastest supercomputers to compute a precise answer.
On the quantum computer, the calculation took less than a thousandth of a second. Each quantum computation was unreliable – fluctuations of quantum noise inevitably creep in and cause errors – but each computation was fast, so it could be performed repeatedly.
In fact, many of the calculations purposely added extra noise, making the answers even more unreliable. But by varying the amount of noise, the researchers were able to figure out the specifics of the noise and its effects at each step of the calculation.
“We can amplify the noise very precisely, and then we can repeat that same circuit,” said Abhinav Kandala, the manager of quantum capabilities and demonstrations at IBM Quantum and an author of the Nature paper. “And once we have the results of these different noise levels, we can extrapolate what the result would have been if there had been no noise.”
Essentially, the researchers were able to subtract the effects of noise from the unreliable quantum calculations, a process they call error mitigation.
“You have to get around that by thinking of really clever ways to reduce the noise,” said Dr. Aaronov. “And this is what they do.”
All told, the computer ran the calculation 600,000 times, resulting in an answer for the overall magnetization produced by the 127 bar magnets.
But how good was the answer?
For help, the IBM team turned to physicists at the University of California, Berkeley. While an Ising model with 127 bar magnets is too large, with far too many possible configurations, to fit into a conventional computer, classical algorithms can yield approximate answers, a technique similar to how compression in JPEG images throws out less crucial data in order to size of the file while preserving most of the details of the image.
Michael Zaletel, a physics professor at Berkeley and an author of the Nature paper, said that when he started working with IBM, he thought his classical algorithms would outperform the quantum algorithms.
“It turned out a little differently than I expected,” said Dr. Zaletel.
Certain configurations of the Ising model can be solved exactly, and both classical and quantum algorithms agreed on the simpler examples. For more complex but solvable cases, the quantum and classical algorithms produced different answers, and it was the quantum algorithm that was correct.
So 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 graduate student at Berkeley who did much of the work. to the classical approaches.
It is not clear that quantum computing is indisputably the winner over classical techniques for the Ising model.
Mr. Anand is currently trying to add a version of error mitigation for the classical algorithm, and it is possible that it will match or exceed the performance of the quantum computations.
“It’s not clear they’ve achieved quantum supremacy here,” said Dr. Zaletel.
In the long run, quantum scientists expect that a different approach, error correction, can detect and correct computational errors, and that will open the door for quantum computers to move forward for many applications.
Error correction is already used in conventional computing and data transmission to fix corruptions. But for quantum computers, error correction is likely years away, requiring better processors that can handle many more qubits.
Error mitigation, according to the IBM scientists, is an intermediate solution that can now be used for increasingly complex problems outside of the Ising model.
“This is one of the simplest science problems out there,” said Dr. Gambetta. “So it’s a good one to start with. But now the question is, how do you generalize it and move on to more interesting science problems?”
Think of discovering the properties of exotic materials, accelerating the discovery of medicines and modeling fusion reactions.
