Kernel methods for SVM and quantum computing
I'm posting this even though I don't have an intelligent question to ask. Only this: I recently took a MOOC on quantum computing (Vazirani at Berkeley from coursera) and then this course, and I'm a little struck by the similarity between the two subjects. If I learned anything in that course, it was that in quantum computing you have an infinite number of parallel processors available for your calculations  but unfortunately no way to get all their results. What you can do is some kind of compression of all those calculations into a single (set of) numbers, like the Fourier transform of all those wave functions sampled at a particular frequency, or other somewhat similar stuff. Then, if you're very lucky, you find that that sampling value will answer some important question. They've managed to find compressions that work to factor large numbers, search N boxes in log N steps, and a number of other interesting calculations that would take huge computing power any other way.
_Anyhow_, I was struck by the professor's explanation of kernel methods, which really sounded exactly the same. Infinite dimensional vector space out there, we're searching it, but we don't need to go there, just use a simple calculation of the kernel/dot product which gives us the essential information we need from that space...
Here I ought to ask a question, but I don't know what it should be. Maybe, can SVMs be a method of gathering information back from the QC multiuniverses?
