Sophisticated actors, including nation-states, are quietly collecting encrypted data with the sole intention of unlocking it once quantum computing matures. Because organizations are legally required to store identity logs, records, and sensitive data for years, that data remains vulnerable to future decryption. A very real strategy called harvest now, decrypt later is already in play.
Blockchains are uniquely exposed. Unlike ephemeral messaging, blockchains permanently secure money, identity, contracts, and governance. Without proactive defenses now, we risk leaving the very foundation of decentralized finance and governance open to tomorrow’s quantum-powered attacks.
If blockchains are to serve as the backbone of finance, governance, and identity, they must be designed for the quantum decade — not with exotic hardware, but with better math.
Traditional cryptography, like RSA, relies on the difficulty of factoring very large prime numbers. For decades, that computational hardness was enough. But in 1994, MIT’s Peter Shor showed that a quantum computer could solve these problems exponentially faster, turning “hard” puzzles into solvable ones.
Post-quantum cryptography (PQC) emerged as a response. Instead of relying on the hardness of a single puzzle, PQC hides data in ways that force attackers to make an infeasible number of guesses. The classic McEliece cryptosystem, introduced in 1978, is still considered one of the strongest PQC approaches. But it comes with a cost: encrypting and decrypting everything under McEliece is so computationally heavy that it’s like a cure that nearly kills the patient along with the disease.
In blockchain, there are a few vital moves that blockchain developers have taken to pragmatically address post-quantum cryptography. The Ethereum Foundation has backed a research group called ZKnox, working on open-source post-quantum solutions that could reduce gas fees by up to 12× while protecting Ethereum’s future against quantum threats. Algorand is securing its entire chain history with FALCON signatures for its post-quantum resilience.
Here’s the key insight: you don’t need to encrypt all the data to make it quantum safe. Decrypting everything is computationally hard and very expensive.
Random Linear Network Coding (RLNC), a coding method developed over two decades in my MIT lab “Network Coding and Reliable Communications Group” offers a proven alternative. RLNC takes data and splits it into coded equations, which can then be mixed and recombined as they travel through the network.
Traditional PQC is costly because it requires encrypting and decrypting all the data. With PQC encryption via RLNC you only need to encrypt a fraction — say, one out of ten coded equations — and the entire dataset inherits its quantum-safe protection.
With RLNC, encrypting 10% of the data effectively protects 100% of the dataset, while cutting 90% of the computational burden. And more importantly, it’s all based on pure math. Because RLNC consists of encoding and decoding data into packets, it can be embedded at any level of the Web3 stack, enabling quantum-safe, scalable performance of decentralized systems.
At the application level, the math is applied at the software layer and managed locally, representing the fastest pace to adoption. At the infrastructure level, we have done a synthesis of RLNC in hardware chips at MIT, showing that this can scale down to silicon and into the core of blockchain nodes, ensuring long-term systemic resilience with minimal cost.
Ultimately, RLNC can also be leveraged as a “quantum-safe memory layer” for blockchains. A way to ensure that data propagation, storage, and writing to the chain inherits quantum security without every transaction being painfully encrypted end-to-end.
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