This breakthrough in quantum computing could, in the near future, change our society.

It has the potential to optimize a lot of things and can com to practical use. For example:

- It can solve complicated optimization problems, such as calculating how to deliver packages in the shortest time while using the least energy. “Optimization problems occur everywhere at every company anywhere in the world,” Bacon said. Addressing those challenges could both save money and help the environment.
- Improving encryption technology by generating random numbers.
- Building machine learning systems better at tasks like distinguishing between real and fake items like bogus political videos.
- Perhaps most interesting, simulating the real physics of molecular-scale materials. Revolutionary developments there could mean more efficient solar panels, a new way to produce nitrogen fertilizer without needing so much energy and better electric car batteries.

- So, if you find this interesting you should read this article published by Stephen Shankland at CNET.com.

“Quantum supremacy” is nice, but more broadly useful quantum computers are probably still a decade away.

On Wednesday, Google published a scientific paper in the journal Nature detailing how its quantum computer vastly outpaced a conventional machine, an idea called quantum supremacy. Powered by a Google-designed quantum processor called Sycamore, it completed a task in 200 seconds that, by Google’s estimate, would take 10,000 years on the world’s fastest supercomputer.

The importance of the achievement can be as hard to understand as quantum computing itself, a field made possible by the mind-bending behavior of atomic-scale physics. But if you want a takeaway, here it is: Quantum computing is only beginning to show some of the promise researchers have hyped for decades. We’re still several breakthroughs away from seeing the true potential fulfilled.

Don’t get me wrong. Google’s achievement, documented by 77 authors in a prestigious peer-reviewed journal, is notable. Quantum computing skeptics should recalibrate their pessimism. Quantum computing ideas that Google has worked on for 13 years, and that famed physicist Richard Feynman described in 1981, are moving into reality.

Quantum computers work by embracing the strange nature of particles at the atomic scale. Where classical computers store data as bits that are either a one or a zero, the quantum computing equivalent, called a qubit, can store information that’s part one and part zero. Next, a quantum computer gangs multiple qubits together, dramatically increasing the number of possible states they can record. Last, processing those qubits lets researchers explore countless possible solutions to a problem simultaneously instead of evaluating them one at a time. It’s lousy for adding two and two, but potentially great for some problems classical computers just can’t cope with.

### Take a look at Google’s quantum computing technology

Google’s quantum researchers are already turning their attention to the next steps needed to make their machines more broadly useful, a step Intel calls quantum practicality.

“It will be a must-have resource at some point,” Hartmut Neven, the researcher who began Google’s quantum computing effort in 2006, said at a press event.

## What’ll quantum computers be good for?

Google’s quantum researchers are excited about the shift in their research from theory to experiment. “I started off … bashing my head against the wall because all the algorithm development was for a machine that didn’t exist,” said Dave Bacon, who leads Google’s quantum software work. Now comes the era when “I can just run it and see what happens.”

Google has a lot of practical uses in mind:

- Complicated optimization problems, such as calculating how to deliver packages in the shortest time while using the least energy. “Optimization problems occur everywhere at every company anywhere in the world,” Bacon said. Addressing those challenges could both save money and help the environment.
- Improving encryption technology by generating random numbers. Google’s quantum team is talking to its encryption key generation team about using a random-number generation tool it’s already developed for today’s Sycamore machine.
- Building machine learning systems better at tasks like distinguishing between real and fake items like bogus political videos. This was the original impetus for Neven’s work, and Google researchers think it could be the first area to deliver on quantum computing’s promise.
- Perhaps most interesting, simulating the real physics of molecular-scale materials. Revolutionary developments there could mean more efficient solar panels, a new way to produce nitrogen fertilizer without needing so much energy and better electric car batteries.

Feynman made this last point back in 1981. “Nature isn’t classical, dammit, and if you want to make a simulation of nature, you’d better make it quantum mechanical,” he said in a speech at MIT. You can approximate nature with a simulation on a classical computer, but Feynman wanted a quantum computer that offers the real thing, a computer that “will do exactly the same as nature,” Feynman said.

Google’s quantum computing competitors, including IBM, Intel and Rigetti Computing, are also eager for better simulation.

## Limits of Google’s quantum supremacy milestone

Quantum supremacy doesn’t mean quantum computers outdo classical computers on every task. Indeed, they’ll always be slower at a lot of crucial processing, meaning they’ll serve alongside classical computers, not replace them.

Physicist Michel Dyakonov at the Université Montpellier in France, remains unconvinced quantum machines will become mainstream. “I don’t believe they will ever become practical,” he said. “The quest for ‘supremacy’ is somewhat artificial and belongs more to the hype than to science. Just show us an elementary quantum calculator that can do three times five or three plus five.”

IBM also challenged Google, arguing in a research paper that a different supercomputing method cuts Google’s 10,000 years down to 2.5 days. IBM’s researchers didn’t question the “quantum supremacy” assertion, though, and Scott Aaronson, a quantum computing researcher at the University of Texas at Austin, said on his blog that even IBM’s alternative is 1,200 times slower using the world’s most powerful classical machine, a supercomputer called Summit.

Quantum computers face serious practical constraints. Qubits, the fundamental units of quantum information processing, are so easily perturbed that they must be housed in complex refrigeration units chilled to a fraction of a degree above absolute zero. Pausing operations to fiddle with the core hardware requires at least two days for the system to warm back up without damage, then restarting requires two more days to cool back down. You won’t find a quantum computer in your laptop anytime soon.

Better qubit stability means a quantum computer can run a longer sequence of operations before tripping up. Right now, a qubit’s useful longevity is about 10 millionths of a second, said Google quantum research scientist Marissa Giustina. “We hope to go up,” she said.

Quantum computing is expensive, too. Google’s Sycamore machines send control signals to the quantum chip using hundreds of cables that each cost $1,000 per 2-foot length. Pushing quantum computers into everyday computing jobs will require years more of heavy, sustained R&D investment.

In a 2019 report, the National Academy of Sciences said that a practical quantum computer is a decade away.

## Google’s quantum optimism

Google believes it’s on the right track, though, and that quantum progress will outstrip classical progress. It looks forward not merely to exponential performance improvements — the kind that Moore’s Law has charted for classical computers — but double exponential improvements.

Google has a long to-do list, starting with improving how long qubits can run error-free. Errors mean a qubit flips to record bad information, stymying a calculation, and improving error rates is the top goal in the next year, said John Martinis, the University of California, Santa Barbara, researcher who now leads Google’s quantum computing hardware team.

“The No. 1 thing we are trying to do is improve the errors of the device,” Martinis said, standing in Google’s lab with five hulking quantum computers suspended around him. “We’ve been kind of ignoring that trying to get to the supremacy result.”

Later will come more fundamental changes, like quantum error correction techniques to sidestep qubit instabilities. Google researchers are unafraid to present plans stretching years into the future, when qubit counts rise from 54 to a million or more. And they’re patient.

“We know that this decade-long march is going to require innovations across theory, engineering and actual physics,” Bacon said.