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This was a cool post. A few nitpicks for anyone else interested in the topic:

(1) a process can be random without being uniform or gaussian

(2) a deterministic process can generate a uniform or gaussian output

(3) chaotic systems are traditionally deterministic by definition. they're deterministic and are sensitive to initial conditions.



Thank you for your comment, indeed!

I think the main confusion for analog implementations of chaotic circuits is that they often have an inherent source of noise (e.g. johnson or flicker noise of resistors, transistors) which will be amplified into large changes by the sensitivity of the system to initial (and also intermediate) conditions.

So the actual implementation has an unpredictable behavior, but this is because the randomness of the components is amplified.

I don't know what the most obvious distinction between a chaotic analog circuit and a TRNG is. For me it was always obvious that any kind of visible structure in the trajectory (the attractors) contradicts randomness. But whenever people see Chua's circuit brought up, there are lots of commends regarding random number generators. It turned into a bit of a pet peeve of mine.


That makes a lot of sense.

I don't know much (or really anything) about circuits and circuit noise.

There is a long history of deterministic pseudo RNGs, which you may already know about. https://en.wikipedia.org/wiki/Pseudorandom_number_generator. These are sometimes chaotic. In this line of thinking, a thing that generates unpredictable noise and adds chaos would make probably a good hardware PRNG.

But the chaotic part is not actually random (although it's hard for attackers to predict). And whether the noise is random depends on a bunch of physics.

But if this has gotten to the point of a pet peeve to you, you might be interested in Randomness Extractors (https://en.wikipedia.org/wiki/Randomness_extractor) which are a way of thinking about questions like "we have an unpredictable source of bits, but it's not as random as it seems... how can we extract actual randomness from it?"

For example, extractors can take low quality somewhat random non-uniform (or non-gaussian) output and use it to create high quality uniform (or gaussian output).


Nice! Thank you for the pointers.




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