[williamjennings]

The efficiency of application to long algorithms and large datasets is a function of available computing power. Certain problems in coding theory can be reduced to homomorphic calculation, but it takes petabytes of data to represent the solution space.

I would recommend Moore's Law as an approximate formula which is useful for guessing what year you will be able to run programs at home on your personal computer. These days, conservative estimates for the number of transistors on a chip should double every 2 years.

[yshalabi]

That was true in an age where single threaded performance also saw similiar improvements. But those days a gone. The power wall and transister scaling also ate factors here.

Today, increases in compute capabilities enabling new technology requires new architectures that match the problems. Similarly to how GPUs enabled deep learning.

[darkmighty]

Can you elaborate on those coding problem reducible to homomorphic calculations? I'm very interested. I thought I heard the converse, homomorphic calculations using coding (my rough understanding of Learning With Errors).

[williamjennings]

How well do you understand the term "homomorphism"?

Search engines are the most common example, in general.

[darkmighty]

Just to be clear, you're referring to coding theory (https://en.wikipedia.org/wiki/Coding_theory) in the sense of geometric codes from information theory, right? I fail to see an obvious way search engines fit in.