Using qubits made from superconducting loops of niobium, cooled to 20 millikelvin above absolute zero to keep them in their lowest energy states, D-Wave’s engineers created a usable computer before even they were sure how it worked.
The company’s 2007 demonstration used a 16-qubit device. By 2011, the D-Wave One machine purchased by Lockheed Martin had 128 qubits. This year’s D-Wave Two, the model acquired by Google and collaborators including NASA, has 512 qubits.
In 2011, D-Wave published evidence for quantum behaviour in its 8-qubit chip. Outside the company, the group that has spent the most time on the question is the University of Southern California’s Quantum Computing Center in Los Angeles, set up in collaboration with Lockheed Martin when the firm bought its D-Wave computer. In April, a team including the centre’s scientific director, Daniel Lidar, circulated results seeming to confirm that the 128-qubit D-Wave One works on a quantum level — although in the fuzzy quantum world nothing is certain, and the results have been challenged.
Still, D-Wave has chipped away at its credibility problem, concludes O’Brien, “and now they’re taken ever more seriously”.
In 2009, for example, a Google research team developed a D-Wave algorithm that could learn to judge whether or not a photo showed a car — an example of a ‘binary image classifier’ that could in principle be used to tell whether a medical image shows a tumor, or a security scan shows a bomb. Finding ever-better ways of doing this sort of task is at the heart of artificial intelligence, and is one area in which an adiabatic quantum computer is expected to excel.
In 2012, researchers at Harvard University in Cambridge, Massachusetts, used a D-Wave machine to find the lowest-energy folding configuration for a protein with six amino acids. They did not have enough qubits to code the problem properly, but even so, on a problem that no other quantum computer could touch, the D-Wave machine found the best solution 13 times out of 10,000 runs. And many of the other answers were good solutions, if not the best.
Lockheed Martin researchers have developed an algorithm that allows D-Wave machines to tell whether a piece of software code is bug-free — something that, they note, is impossible with classical computers. “You would never know” for sure if a piece of classical-computer code was clean, says Ray Johnson, chief technology officer for Lockheed Martin in Bethesda, Maryland. All anyone could say was that no fault had been found after years of testing. “But now you can say with certainty,” says Johnson. “We have great hope, and confidence, in the ability of the computer to scale to real-world complex problems.”
CTO Rose on the future of Dwave
DWave CTO Rose is convinced that D-Wave’s next generation will prove that it can solve exponentially more difficult problems without taking exponentially more time. “There’s going to be absolutely no hope for classical computers if this thing next year behaves as we expect,” he says. Rose goes so far as to consider the hardware problem solved: the real challenge, he says, will be the software. “Programming this thing is ridiculously hard,” he admits; it can take months to work out how to phrase a problem so that the computer can understand it. But D-Wave has teams working on that — including Rose.
Gate Model Quantum Computer Competition
Work continues to make qubits for universal gate-model quantum computers more reliable, or easier to mass-produce. O’Brien [he made the quantum computer that factored 15], who admits that his 4-year-old daughter can factorize 21 faster than his computer, is optimistic about the future. “In 10 years’ time, I’d be hugely disappointed if we didn’t have a machine capable of factoring a 1,000-bit number, involving millions of qubits,” he says.
SOURCE – Journal Nature
Brian Wang is a Futurist Thought Leader and a popular Science blogger with 1 million readers per month. His blog Nextbigfuture.com is ranked #1 Science News Blog. It covers many disruptive technology and trends including Space, Robotics, Artificial Intelligence, Medicine, Anti-aging Biotechnology, and Nanotechnology.
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