Saturday, December 19, 2015

Qubits: "Race to vastly better annealers, powerful universal quantum computers which will transform machine learning into quantum learning" (GOOG; IBM; MSFT)

From Next Big Future:
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.
“Machine-learning algorithms are really kind of stupid,”...“They need so many examples to learn.”
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....MORE 

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