Understanding QRNG

General
#1

Hey all,

I know I am a bit late to the party, but I’ve been trying to read a bit more about the QRNG product launched a few weeks back. I saw the host of media articles that talk about the post at a high level but I have not found any more detailed documents of the product and how it works exactly (Would love to if they exist).

I am still trying to understand exactly how the QRNG works, but from my understanding ANU run’s an off chain process for generation Quantum Random Numbers, that is then connected via an oracle to emit these numbers on-chain. What verifies that the number being generated is the number being pass on-chain. Is there any verification on how ANU’s QRNG process works, but from a theory perspective, but also in terms of a practical implementation? I would just love to learn a bit more on how this works / what the the limitations of this product.

Any thoughts / links you think would be relevant would be greatly appreciated

Thanks @finkbeca

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#2

The best descriptions of how it works on the ANU side are in AQN’s FAQ, their old FAQ, and these papers: Appl. Phys. Lett. 98, and Phys. Rev. Applied 3.

Then the AQN team runs an Airnode in the same AWS environment where their API is served to provide these numbers to blockchains without introducing a 3rd-party middleman. The Web3 documentation is here.

You trust AQN that the numbers being generated are in fact the numbers being served by the API. They have a zero-knowledge architecture in place so they never even see the numbers. But you can easily request a sample of numbers from the API and verify that it’s a uniform random distribution. Likewise, you can get a sample of the random numbers that have been delivered on various chains by the AQN Airnode and verify the distribution of those.

With first-party oracles we’re basically acknowledging and asserting that when it comes to off-chain data you have to trust the API providers anyway and the most secure, risk-minimized way to do that is to have them serve the data themselves and stake their reputation on the fact that they’re proving honest, high-quality data.

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