I'm sure nearly everyone has by now heard that Google "proved" the D-Wave 2 they operate jointly with NASA (mainly paid for by Google) can operate "up to ∼ 108 times faster". If not, you might want to have a quick read of their paper, titled "What is the Computational Value of Finite Range Tunneling?" Even if you don't read the paper, you can be excused for wondering if "the race to a real quantum computer" (my words) is over. There is enough press around every such announcement from Google, IBM, and others that you easily can get the impression it is just down to the details now.
In fact, there are still major, fundamental challenges to be overcome and building blocks to be created. You might have heard earlier last year how IBM had solved the problem of quantum error correction. Although there was a lot of buzz about that, it is interesting to note that a month earlier, Google reported on their own blog that they had already done more or less the same thing. And so it goes. But the near-term reality could be even more interesting, especially if you are old or lucky enough to have played with analog computing.
Way back in 2013, I wrote a series of posts on the possible role of quantum computing in analog design. (see: Will Quantum Computing Enhance Analog Design? Part 1 Parts 1-3) In the introduction to that series, I provided the following diagram:
An analog problem.
Consider if you were given the problem of making a table, sort of like a train schedule, to represent flow out of the 5 outlets versus various combinations of the inlets. To solve that problem in digital space requires a pretty good fluid dynamics simulation tool, an accurate physical model of the system translated into the input for the tool, and a fast computer. It is very doable, but pretty expensive.
Now suppose we change the problem to determine which input has the most impact on the output of the 3rd pipe from the left? You could solve that by going to the hardware store and plumbing up a simulator, which would be a version of an analog computer. Or, you could build an analog computer, and come up with models for the fittings (combinations of L, R, C circuits) then play with it. The result for any given set of inputs, once the analog computer is in hand and "programmed', is available nearly instantly, while each run of the digital simulation might take minutes or hours or days. If you think about the fittings modeled as L-R-C circuits, they capture the dynamics as well as the steady state behavior. The dynamic part can be thought of as a simulator node capable of having many possible values. And that sounds something like a qubit.