I'll let him comment on what you guys were talking about but I know of his theory that you are commenting on. But I'm pretty sure you have misunderstood what he was talking about in terms of what sparsey does vs his ideas on parts of it (SDRs specifically) being framed as an alternative to quantum computing. You should have linked his direct thoughts on the qunatum computing parts, instead of throwing all of Sparsey under the bus.

http://sparsey.com/SDR_and_QC.html

You may have not directly called it quackery but you suggested and alluded to it requiring a bogosity meter and that quacks are attracted to it. It's pretty clear what you meant, to be honest.

Sparsey in general though is a separate subject really. What it is doing is capturing all the features it has ever seen and building temporal and spatial relations between those features and then storing those as a code that represents an input in a particular spatial and temporal context. So if I have the words CAT and BAT the A in CAT and the A in BAT will have different codes because of the letter that came before it, likewise with T because A's code is different for both C and B therefore so is T's in both separate cases. So I think that is where the whole quantum part comes into his ideas. Every sample in a sequence has many possible "realities" or codes that can represent it and by representing it in a certain way you are also representing an entire sequence that came before it and a branching point of possible future representations. I have near zero experience with quantum mechanics so I won't comment on how closely it is or isn't to that, because I have no clue.

More importantly though, even if it doesn't represent an alternative to mathematically tit for tat exactness for quantum computing, what it can do is reduce computation times dramatically. The idea is that for an equation you could give a sufficiently sized sparsey model a number of inputs. Sparsey naturally represents those inputs as some representation and you can assign the answer to that representation, and you do that with all or as many inputs as possible, each getting its own unique representation. So basically what you are doing is making a giant lookup table of answers. So when you see a set of inputs you know the answer via code lookup rather than calculating it. Sparsey is a bit memory heavy so you would only really want to do that with large hard problems. But that's where the speed comes in, you know instantly the entire state, its past, and its likely conclusion (because it probabilistic), or in fact all of its possible conclusions.

It's similar to Q Learning in some ways, in the sense that you are storing many many states you have encountered. Except sparsey doesn't store the states brute force like qlearning. Similar states, spatially, temporally or both, are stored with more code overlap, dissimilar ones have less overlap.

But that's the gist of it. He has some results posted. We've talked about getting some sort of public repository up and running for people to play around with and maybe a community forum or something, but that's one of his concerns lately, letting people see the results for themselves rather than taking his word for it. Which, I have one project I need to finish right now, but I will be working on that full time sometime in May.