Generating and using randomness practically remains an enormously rich field of research.
Randomness sounds like the simplest thing in the world. Flip a coin, and it lands heads or tails; toss a die, and you have a 1 in 6 chance of guessing the number. But in practice, true randomness is surprisingly hard to pinpoint.
Researchers who work on randomness tend to sidestep the philosophical aspects of whether true randomness exists and instead work on randomness from a practical perspective: a source is random enough if no test, algorithm, or adversary can reliably predict it, compress it, or distinguish it from an ideal stream of fair coin flips. In a sense, it's practical randomness rather than true randomness.
This being said, how do you figure out if something is random enough to trust? Or in other words, given a messy, real-life source of information, can we transform its biased, correlated noise into bits that no efficient adversary can distinguish from ideal randomness?
Check out the full article by Andrei Mihai here: The 30-Year Randomness Problem That Could Help Secure the Quantum Future
Image caption: Image credits: Riho Kroll / Unsplash.