Quantum Algorithms (run on quantum computers) are the future for Machine Learning. This article was from 2009 though, and I haven't seen a whole lot of progress in the field. Artificial Neural Networks that can model every possible value of every node at the same time is very powerful.
I don't buy it. Quantum computing is extremely important, ground breaking even for complexity theory and theoretical computer science in general. Quantum computing is to complexity theory what the real numbers are to number theory.
From a practical point of view though there are only two interesting family of algorithms: Shor's Quantum Fourier transform and Grover's square root speedup for searching an unstructured list (give or take). The former doesn't seem very applicable to machine learning (yet?). The later is AFAI am concerned useless for machine learning: if we really want to speed up searching some unordered structure then I think we will rely on sorting for a very long time to come - the advantage is much more impressive than Grover's square root speedup.
May I remind you that in the past 20 years there hasn't been much progress on "practical quantum algorithms" (that is not to say quantum complexity results). I think until something brand new comes out of the quantum computing community, these quantum claims are great PR but that's where it ends ...
That is not how it works Viscanti, gate-based QCs are still toys and it remains to be proved if they will become useful anytime soon. On the other hand D-Wave's adiabatic chip is very promising but it requires you to fit a "square peg in a round hole" - expressing problems into a format their hardware can handle is tricky.
