I don't believe it is the result of a LLM, more like an oversimplification, or maybe a minor fuckup on the part of the author as simple majority voting is often used in redundant systems, just not for memories as there are better ways.

And for a LLM result, this is what ChatGPT says when asked "How does memory error correction differ from quantum error correction?", among other things.

> Relies on redundancy by encoding extra bits into the data using techniques like parity bits, Hamming codes, or Reed-Solomon codes.

And when asked for a simplified answer

> Classical memory error correction fixes mistakes in regular computer data (0s and 1s) by adding extra bits to check for and fix any errors, like a safety net catching flipped bits. Quantum error correction, on the other hand, protects delicate quantum bits (qubits), which can hold more complex information (like being 0 and 1 at the same time), from errors caused by noise or interference. Because qubits are fragile and can’t be directly measured without breaking their state, quantum error correction uses clever techniques involving multiple qubits and special rules of quantum physics to detect and fix errors without ruining the quantum information.

Absolutely no mention of majority voting here.

EDIT: GPT-4o mini does mention majority voting as an example of a memory error correction scheme but not as the way to do it. The explanation is overall more clumsy, but generally correct, I don't know enough about quantum error correction to fact-check.

People always have made bad assumptions or had misunderstandings. Maybe the author just doesn't understand ECC and always assumed it was consensus-based. I do things like that (I try not to write about them without verifying); I'm confident that so do you and everyone reading this.

>Maybe the author just doesn't understand ECC and always assumed it was consensus-based.

That's likely, or it was LLM output and the author didn't know enough to know it was wrong. We've seen that in a lot of tech articles lately where authors assume that something that is true-ish in one area is also true in another, and it's obvious they just don't understand other area they are writing about.

Frankly every state of the art LLM would not make this error. Perhaps GPT3.5 would have, but the space of errors they tend to make now is in areas of ambiguity or things that require deductive reasoning, math, etc. Areas that are well described in literature they tend to not make mistakes.

I've gone to lots of talks on quantum error correction, and most of them start out with explaining the repetition code. Not because it's widely used, but because it is very easy to explain to someone who knows nothing about classical coding theory. And because the surface code is essentially the product of two repetition codes, so if you want to understand surface code quantum error correction you don't need to understand any classical codes besides the repetition code.

All that is to say that someone who had been to a few talks on quantum error correction but didn't directly work on that problem might reasonably believe that the repetition code is an important classical code.