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John Martinis has been researching how quantum computers could work for 30 years. Now he could be on the verge of finally making a useful one.

John Martinis used the arm of his reading glasses to indicate the spot where he intends to demonstrate an almost unimaginably powerful new form of computer in a few years. It is a cylindrical socket an inch and a half across, at the bottom of a torso-sized stack of plates, blocks, and wires of brass, copper, and gold. The day after I met with him this fall, he loaded the socket with an experimental superconducting chip etched with a microscopic Google logo and cooled the apparatus to a hundredth of a degree Celsius above absolute zero. To celebrate that first day of testing the machine, Martinis threw what he called “a little party” at a brewpub with colleagues from his newly outfitted Google lab in Santa Barbara, California.

That party was nothing compared with the celebration that will take place if Martinis and his group can actually create the wonder computer they seek. Because it would harness the strange properties of quantum physics that arise in extreme conditions like those on the ultracold chip, the new computer would let a Google coder run calculations in a coffee break that would take a supercomputer of today millions of years. The software that Google has developed on ordinary computers to drive cars or answer questions could become vastly more intelligent. And earlier-stage ideas bubbling up at Google and its parent company, such as robots that can serve as emergency responders or software that can converse at a human level, might become real.

The theoretical underpinnings of quantum computing are well established. And physicists can build the basic units, known as qubits, out of which a quantum computer would be made. They can even operate qubits together in small groups. But they have not made a fully working, practical quantum computer.

Martinis is a towering figure in the field: his research group at the University of California, Santa Barbara, has demonstrated some of the most reliable qubits around and gotten them running some of the code a quantum computer would need to function. He was after persuading the company that his team’s technology could mature rapidly with the right support. With his new Google lab up and running, Martinis guesses that he can demonstrate a small but useful quantum computer in two or three years. “We often say to each other that we’re in the process of giving birth to the quantum computer industry, ” he says.

Google and quantum computing are a match made in algorithmic heaven. The company is often said to be defined by an insatiable hunger for data. But Google has a more pressing strategic addiction: to technology that extracts information from data, and even creates intelligence from it. The company was founded to commercialize an algorithm for ranking Web pages, and it built its financial foundations with systems that sell and target ads. More recently, Google has invested heavily in the development of AI software that can learn to understand language or images, perform basic reasoning, or steer a car through traffic—all things that remain tricky for conventional computers but should be a breeze for quantum ones. “Machine learning is a core, transformative way by which we’re rethinking how we’re doing everything, ” Google’s CEO, Sundar Pichai, recently informed investors. Supporting that effort would be the first of many jobs for Martinis’s new quantum industry.

Dream maker

As recently as last week the prospect of a quantum computer doing anything useful within a few years seemed remote. Researchers in government, academic, and corporate labs were far from combining enough qubits to make even a simple proof-of-principle machine. A well-funded Canadian startup called D-Wave Systems sold a few of what it called “the world’s first commercial quantum computers” but spent years failing to convince experts that the machines actually were doing what a quantum computer should (see “The CIA and Jeff Bezos Bet on Quantum Computing”).

Then NASA summoned journalists to building N-258 at its Ames Research Center in Mountain View, California, which since 2013 has hosted a D-Wave computer bought by Google. There Hartmut Neven, who leads the Quantum Artificial Intelligence lab Google established to experiment with the D-Wave machine, unveiled the first real evidence that it can offer the power proponents of quantum computing have promised. In a carefully designed test, the superconducting chip inside D-Wave’s computer—known as a quantum annealer—had performed 100 million times faster than a conventional processor.

As recently as last week the prospect of a quantum computer doing anything useful within a few years seemed remote. Then NASA summoned journalists to its Ames Research Center in Mountain View.

However, this kind of advantage needs to be available in practical computing tasks, not just contrived tests. “We need to make it easier to take a problem that comes up at an engineer’s desk and put it into the computer, ” said Neven, a talkative machine-learning expert. That’s where Martinis comes in. Neven doesn’t think D-Wave can get a version of its quantum annealer ready to serve Google’s engineers quickly enough, so he hired Martinis to do it. “It became clear that we can’t just wait, ” Neven says. “There’s a list of shortcomings that need to be overcome in order to arrive at a real technology.” He says the qubits on D-Wave’s chip are too unreliable and aren’t wired together thickly enough. (D-Wave’s CEO, Vern Brownell, responds that he’s not worried about competition from Google.)

Google will be competing not only with whatever improvements D-Wave can make, but also with Microsoft and IBM, which have substantial quantum computing projects of their own (see “Microsoft’s Quantum Mechanics” and “ IBM Shows Off a Quantum Computing Chip ”). But those companies are focused on designs much further from becoming practically useful. Indeed, a rough internal time line for Google’s project estimates that Martinis’s group can make a quantum annealer with 100 qubits as soon as 2017. D-Wave’s latest chip already has 1, 097 qubits, but Neven says a high-quality chip with fewer qubits will probably be useful for some tasks nonetheless. A quantum annealer can run only one particular algorithm, but it happens to be one well suited to the areas Google most cares about. The applications that could particularly benefit include pattern recognition and machine learning, says William Oliver, a senior staff member at MIT Lincoln Laboratory who has studied the potential of quantum computing.

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