December 19, 2015

Race to vastly better annealers, powerful universal quantum computers which will transform machine learning into quantum learning

Google has a team led by John Martinis to develop better quantum computers. They 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. But IBM and Microsoft 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.

Google 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.)

Martinis and his team have to be adept at many things because qubits are fickle. They can be made in various ways—Martinis uses aluminum loops chilled with tiny currents until they become superconductors—but all represent data by means of delicate quantum states that are easily distorted or destroyed by heat and electromagnetic noise, potentially ruining a calculation.

Qubits use their fragile physics to do the same thing that transistors use electricity to do on a conventional chip: represent binary bits of information, either 0 or 1. But qubits can also attain a state, called a superposition, that is effectively both 0 and 1 at the same time. Qubits in a superposition can become linked by a phenomenon known as entanglement, which means an action performed on one has instant effects on the other. Those effects allow a single operation in a quantum computer to do the work of many, many more operations in a conventional computer. In some cases, a quantum computer’s advantage over a conventional one should grow exponentially with the amount of data to be worked on.

The coherence time of Martinis qubits, or the length of time they can maintain a superposition, is tens of microseconds—about 10,000 times the figure for those on D-Wave’s chip.

Martinis’s confidence in his team’s hardware even has him thinking he can build Google an alternative to a quantum annealer that would be even more powerful. A universal quantum computer, as it would be called, could be programmed to take on any kind of problem, not just one kind of math. The theory behind that approach is actually better understood than the one for annealers, in part because most of the time and money in quantum computing research have been devoted to universal quantum computing. But qubits have not been reliable enough to translate the theory into a working universal quantum computer.

Martinis and his team became the first to demonstrate qubits that crossed a crucial reliability threshold for a universal quantum computer. They got a chip with nine qubits to run part of an error-checking program, called the surface code, that’s necessary for such a computer to operate

This experimental chip, etched with the Google logo, is cooled to just above absolute zero in order to generate quantum effects.

This structure of metal plates is necessary to cool and shield quantum chips

Martinis aims to show off a complete universal quantum computer with about 100 qubits around the same time he delivers Google’s new quantum annealer, in about two years. That would be a milestone in computer science, but it would be unlikely to help Google’s programmers right away. Such is the complexity of the surface code that although a chip with 100 qubits could run the error-checking program, it would be unable to do any useful work in addition to that, says Robert McDermott, who leads a quantum computing research group at the University of Wisconsin. Yet Martinis thinks that once he can get his qubits reliable enough to put 100 of them on a universal quantum chip, the path to combining many more will open up. “This is something we understand pretty well,” he says. “It’s hard to get coherence but easy to scale up.”

Powerful Quantum Computers are the key to powerful machine learning

When Martinis explains why his technology is needed at Google, he doesn’t spare the feelings of the people working on AI. “Machine-learning algorithms are really kind of stupid,” he says, with a hint of wonder in his voice. “They need so many examples to learn.”

Indeed, the machine learning used by Google and other computing companies is pathetic next to the way humans or animals pick up new skills or knowledge. Teaching a piece of software new tricks, such as how to recognize cars and cats in photos, generally requires thousands or millions of carefully curated and labeled examples. Although a technique called deep learning has recently produced striking advances in the accuracy with which software can learn to interpret images and speech, more complex faculties like understanding the nuances of language remain out of machines’ reach.

Figuring out how Martinis’s chips can make Google’s software less stupid falls to Neven. He thinks that the prodigious power of qubits will narrow the gap between machine learning and biological learning—and remake the field of artificial intelligence. “Machine learning will be transformed into quantum learning,” he says. That could mean software that can learn from messier data, or from less data, or even without explicit instruction. For instance, Google’s researchers have designed an algorithm they think could allow machine-learning software to pick up a new trick even if as much as half the example data it’s given is incorrectly labeled. Neven muses that this kind of computational muscle could be the key to giving computers capabilities today limited to humans. “People talk about whether we can make creative machines–the most creative systems we can build will be quantum AI systems,” he says.

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