Google Reports Progress on a Shortcut to Quantum Supremacy
Quantum computers could be closer than we thought, thanks to new steps toward an easier way to build them.
A computer that uses the quirks of quantum physics to work on data should be capable of things far beyond any machine in use today. Governments and large tech companies have spent huge sums trying to prove out that idea. Yet quantum computers have sometimes seemed like one of those technologies that are always 20 years away.
Recently some leading research groups have come to think they can see a path to shortening that time considerably. Yesterday Google and researchers from the University of the Basque Country in Bilbao, Spain, published results that could lead to a shortcut to the long-awaited first conclusive demonstration of the power of quantum computing.
The new result is one of the first fruits of a plan Google’s quantum researchers laid out when I visited their new lab last year.
At the heart of that effort is a decision to move away from a design that has so far sucked up most of the field’s money and effort. Digital quantum computers, as they are called, are modeled on the way our computers work today, and the design has been rigorously proved out by theorists. But they would require a lot of error-correcting hardware and software to compensate for the delicacy of the quantum effects they use to work with data.
These designs work less like a conventional computer and are less well understood theoretically. And they would still need a way to deal with errors. But the burden of error correction should be much smaller. As a result, it should be much easier to demonstrate the power of a quantum computer this way.
The leader of Google’s quantum hardware project, veteran researcher John Martinis, told me last year that it could take just a few years to make a chip complex enough to do that. The paper his group and researchers at the University of the Basque Country published Wednesday shows progress is being made.
The team used the analog quantum computing approach to program a superconducting quantum chip to simulate nine atoms interacting magnetically. That was made possible by drawing on some of the error correction techniques developed in earlier work on the harder-to-scale-up digital quantum computing.
The chip used had nine of the basic building blocks of a quantum computer, known as qubits. It would take an analog quantum computer with 40 or more to demonstrate what researchers charmingly call “quantum supremacy”—meaning a system that can conclusively demonstrate things impossible for a conventional computer. (Startup D-Wave Systems has demonstrated chips with over 1,000 qubits, but despite promising results, they have not been conclusively proved to confer the benefits of a quantum computer.)
Google says it can scale up to that point relatively quickly, and other researchers in the field say it’s credible.
It would likely take scaling up a little further to do useful work with an analog quantum computer. If and when Google or some other company does that, the devices could be used to crack tough chemistry problems in health or energy by simulating atoms to a level of realism impossible today.
Google also believes that quantum supremacy could advance its research in machine-learning and artificial-intelligence technology, which underpins CEO Sundar Pichai’s claim that the company has entered an “AI first” era.
Hartmut Neven, who leads Google’s work on figuring out what to do with quantum computers once they arrive, hopefully told me last year that the power of quantum-enhanced artificial intelligence could sweep away today’s technology. “I would predict that in 10 years there’s nothing but quantum machine learning—you don’t do the conventional way anymore,” he said.
(Read more: Nature, “The Tiny Startup Racing Google to Build a Quantum Computing Chip,” “Google Finally Launches a Siri Killer in Pivot Away from Conventional Search,” “Google Says It Has Proved Its Controversial Quantum Computer Really Works,” “Google’s Quantum Dream Machine”)
Hear more about machine learning from the experts at the EmTech Digital Conference, March 26-27, 2018 in San Francisco.Learn more and register