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If that's too abstract, the usual way quantum annealing is explained is by an analogy with finding the lowest point in a mountainous landscape. A classical computer would do it like a solitary walker who slowly wandered over the whole landscape, checking the elevations at each point, one by one. A quantum computer could send multiple walkers at once swarming out across the mountains, who would then all report back at the same time. In its ability to pluck a single answer from a roiling sea of possibilities in one swift gesture, a quantum computer is not unlike a human brain.
Once Rose and D-Wave had committed to the adiabatic model, they proceeded with dispatch. In 2007 D-Wave publicly demonstrated a 16-qubit adiabatic quantum computer. By 2011 it had built (and sold to Lockheed Martin) the D-Wave One, with 128 qubits. In 2013 it unveiled the 512-qubit D-Wave Two. They've been doubling the number of qubits every year, and they plan to stick to that pace while at the same time increasing the connectivity between the qubits. "It's just a matter of years before this capability becomes so powerful that anyone who does any kind of computing is going to have to take a very close look at it," says Vern Brownell, D-Wave's CEO, who earlier in his career was chief technology officer at Goldman Sachs. "We're on that cusp right now."
But we're not there yet. Adiabatic quantum computing may be technically simpler than the gate-model kind, but it comes with trade-offs. An adiabatic quantum computer can really solve only one class of problems, called discrete combinatorial optimization problems, which involve finding the best--the shortest, or the fastest, or the cheapest, or the most efficient--way of doing a given task. This narrows the scope of what you can do considerably.
For example, you can't as yet perform the kind of cryptographic wizardry the NSA was interested in, because an adiabatic quantum computer won't run the right algorithm. It's a special-purpose tool. "You take your general-purpose chip," Rose says, "and you do a bunch of inefficient stuff that generates megawatts of heat and takes forever, and you can get the answer out of it. But this thing, with a picowatt and a microsecond, does the same thing. So it's just doing something very specific, very fast, very efficiently."
This is great if you have a really hard discrete combinatorial optimization problem to solve. Not everybody does. But once you start looking for optimization problems, or at least problems that can be twisted around to look like optimization problems, you find them all over the place: in software design, tumor treatments, logistical planning, the stock market, airline schedules, the search for Earth-like planets in other solar systems, and in particular in machine learning.