Can we use computational algorithms to make accurate predictions of physical phenomena? In this talk, intended for non-experts, I will give examples where complicated space-time phenomena can be exquisitely captured with simple computational algorithms, that not only produce patterns resembling those seen in experiment, but also make accurate predictions about probes of dynamics and spatial organisation, such as correlation functions. I use examples from condensed matter physics, as well as from geophysics.
We construct linear codes of almost-linear length and linear distance that can be locally self-corrected on average from a constant number of queries:
1. Given oracle access to a word $w\in\Sigma^n$ that is at least $\varepsilon$-close to a codeword $c$, and an index $i\in [n]$ to correct, with high probability over $i$ and over the internal randomness, the local algorithm returns a list of possible corrections that contains $c_i$.
In recent joint work with Alex Arkhipov, we proposed a quantum optics experiment, which would sample from a probability distribution that we believe cannot be sampled (even approximately) by any efficient classical algorithm, unless the polynomial hierarchy collapses. Several optics groups are already working toward doing our experiment.
This talk will be a biased survey of recent work on various properties of elements of infinite groups, which can be shown to hold with high probability once the elements are sampled from a large enough subset of the group (examples of groups: linear groups over the integers, free groups, hyperbolic groups, mapping class groups, automorphism groups of free groups . . . )