"This paper presents implementations that match and, where possible, exceed current quantum factorisation records using a VIC-20 8-bit home computer from 1981, an abacus, and a dog.

We hope that this work will inspire future efforts to match any further quantum factorisation records, should they arise."

Note that this is three attempts to match current quantum computing records, not a single attempt utilizing all three tools.

(The IACR is a legit cryptology organization. Been around for years and years.)

https://eprint.iacr.org/2025/1237.pdf

(h/t @cstross )

so, #sysadmin sorts: chill your quantum computing worries

hastable tiny pointers

In computer science, a trie (/ˈtraɪ/, /ˈtriː/), also called digital tree or prefix tree,[1] is a type of k-ary search tree, a tree data structure used for locating specific keys from within a set. These keys are most often strings, with links between nodes defined not by the entire key, but by individual characters. In order to access a key (to recover its value, change it, or remove it), the trie is traversed depth-first, following the links between nodes, which represent each character in the key.

Visualization is a fundamental part of modern data-centered applications: a plot can show you in the blink of an eye if your data has the shape that you’re expecting, but having to retrieve all your samples just to cram them in a graph without enough pixels to show them all is clearly not a good idea.

Downsampling seems the obvious next step, but how to choose which samples to keep and which to throw away? The key idea is to take the samples that make the overall shape of your data as similar to the original one as possible.

generation de bitmap à partir de pattern d'image