Gianluca Di Bella, a smart-contract researcher specializing in zero-knowledge proofs, said the danger posed by quantum computing isn’t a distant concern; it’s a current one.

Speaking to Cointelegraph at the UN City offices in Copenhagen, 

Post-quantum zero-knowledge proofs

Once — or if, according to some — quantum computing reaches the necessary power and scale, it could undermine the security assumptions of traditional encryption and zero-knowledge proofs. This could result in encrypted data being decrypted and proofs generated by traditional ZK-proofs being forged, faking valid statements or bypassing verification.

Several post-quantum encryption standards already exist, with some approved by the National Institute of Standards and Technology (NIST) — specifically ML-KEM, ML-DSA and SLH-DSA. But no comparable post-quantum ZK-proof standard has reached maturity. This is an area of research that Di Bella engages with through the smart contract development company he co-founded, Mood Global Services.

Di Bella pointed to Permutations over Lagrange bases for Oecumenical Noninteractive arguments of Knowledge (PLONK) as a post-quantum ZK-proof implementation. Still, they are not “battle tested” and are currently seen as a research implementation.

Related: Quantum threat to Bitcoin still years away, says Borderless Capital partner

A long road ahead

Discussing how long he expects PLONK development will take to reach a stage suitable for real-world usage, Di Bella noted that it is challenging to make accurate timeline predictions and lamented the lack of investment in the sector. He noted that it is a niche subject and engaging with it requires significant specialized knowledge, which tends to decrease investment and slow development.

“If you are a research and development manager of any corporation, you don’t invest in something that you don’t understand,“ he said.

Di Bella said ZK-proof development is carried out in low-level Rust programming with little abstraction and high complexity. In a way, most ZK-proof systems are programmed in a manner reminiscent of the complexity of early programming.

While we are now used to high-level programming languages abstracting complexity, programming this kind of system is “definitely math again,” Di Bella said.

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