I'm truly conviced that exploring landscapes is the way to get higher abstraction skills in the design domain. Yet, discussing this point with someone in the field, they told me we're still lacking a major breakthrough, since right now we only have several (more or less interesting/successful and still) arbitrary approaches to the problem, each of them more fit for one type of problems.
It is expected that someone in academia develops the whole mathematical apparatus needed for the rest of us to keep wrong steps to a minimum. I believe until that point we will not get closer to a general solution, although of course the more knowledge we hoard about particular cases, the better!
I'll contact you regarding those positions :D
Cheers!
PS: out of curiosity, with what version of MNIST do you work? Binary (B&W, 0 or 1) or grayscale? And what image resolution?
This is actually a very cool demo! I did run some simulations similar to this one, but at some point I stopped trying so many different models for the same problem and started facing more difficult tasks (like multi-class, for example).
If it is to anyone's interest, the problem with escalation of this model lies on what would be the analogous to "activation functions" in classic neural networks. The cool thing about the sigmoid function is that it's monotonous, whereas every-day quantum gates are deeply rooted in periodic functions, so the chances of getting stuck inside a bad local minimum rise absurdly fast when deeper or wider circuits are considered.
Actually the problem shown on the demo was pretty much identical to the initial project for my bachelor's thesis, and some phd students took it and raised it to a whole new level (https://arxiv.org/abs/1907.02085). In order to understand the purpose of the cost functions and such, some notions of quantum mechanics come in handy, but if you believe that what we did just so happens to make some underlying sense, then the ultimate result is stunning: there is an implementation which circumvents the problem of local minima, and renders meaningful results at attainably small scales.
It is expected that someone in academia develops the whole mathematical apparatus needed for the rest of us to keep wrong steps to a minimum. I believe until that point we will not get closer to a general solution, although of course the more knowledge we hoard about particular cases, the better!
I'll contact you regarding those positions :D
Cheers!
PS: out of curiosity, with what version of MNIST do you work? Binary (B&W, 0 or 1) or grayscale? And what image resolution?