For years, quantum computing has sounded like a distant problem for the blockchain industry—something to worry about once machines become powerful enough to crack today’s cryptography. Muriel Médard, an MIT professor and Optimum co-founder, argues that the more urgent answer is already sitting in classical mathematics.
That matters because some of the most sensitive information being encrypted today could be collected now and decoded later. Known as “harvest now, decrypt later,” the strategy involves storing encrypted data until quantum computers become capable of breaking the protections around it.
Blockchains have a particularly uncomfortable relationship with that threat. Unlike a message that disappears into an inbox, blockchain records are designed to persist. Money, identity data, contracts and governance records can remain accessible for years.
That makes quantum resistance less like a futuristic upgrade and more like a design decision that has to happen before the machines arrive. The idea behind a quantum-proof blockchain, Médard argues, is not to build a faster quantum computer. It is to use better mathematics.
The cryptography problem hiding in plain sight
Modern cryptography often depends on mathematical problems that are extremely difficult for conventional computers. RSA, for example, relies on the challenge of factoring enormous prime numbers.
Peter Shor changed the conversation in 1994 when he demonstrated that quantum computers could solve this class of problems dramatically faster. What looks computationally hopeless today could eventually become tractable with sufficiently capable quantum hardware.
Post-quantum cryptography, or PQC, was developed as a response. Rather than betting everything on one mathematical problem, these systems use constructions designed to remain difficult even for quantum attackers.
One of the earliest and most durable examples is the McEliece cryptosystem, introduced in 1978. It remains widely regarded as a strong PQC approach, but its computational demands are considerable. Encrypting and decrypting large amounts of information at that scale can become expensive enough to undermine the system it is meant to protect.
Blockchain developers have already started looking for more practical approaches. The Ethereum Foundation has supported the research group ZKnox on open-source post-quantum technology that could reduce gas costs by as much as 12 times while preparing Ethereum for quantum threats.
Algorand, meanwhile, is using FALCON signatures to secure its full chain history and strengthen its resistance to future quantum attacks.
What makes a quantum-proof blockchain different?
Médard’s proposal starts with a deceptively simple idea: protecting everything does not necessarily mean encrypting everything.
Her MIT lab, the Network Coding and Reliable Communications Group, has spent more than two decades developing Random Linear Network Coding, or RLNC. The technique breaks information into coded equations that can be combined and reconstructed as they move through a network.
That creates an intriguing shortcut for post-quantum protection.
Instead of encrypting every coded piece of a dataset, RLNC can allow only a fraction of the equations to be encrypted while the resulting information remains protected as a whole.
The example in the proposal is striking: encrypting roughly one in 10 coded equations could protect the entire dataset, reducing the computational workload by about 90%.
For a quantum-proof blockchain, that distinction could be significant. Blockchain infrastructure has to move, store and process persistent information at scale, so applying expensive encryption to every piece of data can quickly become a performance problem.
RLNC can instead be incorporated at different points in the Web3 stack. At the application level, it can run through software and local processing, potentially making adoption faster.
The same concept can also move closer to the hardware. Researchers at MIT have already demonstrated RLNC implementations in silicon, suggesting the approach could eventually sit inside the infrastructure that runs blockchain nodes.
A quantum-proof blockchain may need a memory layer
That leads to a broader possibility: using RLNC as a kind of quantum-safe memory layer for blockchain systems.
Rather than forcing every transaction through heavyweight end-to-end encryption, the mathematical coding layer could help protect information as it is propagated, stored and written to the chain.
The attraction is not just security. It is the possibility of preserving performance while preparing infrastructure for a threat that does not yet fully exist.
And that timing matters. Institutions considering public blockchains for finance, identity or governance are unlikely to be comfortable betting critical records on cryptography that could eventually become obsolete.
Médard’s argument is ultimately less about quantum computing than it is about preparation. Waiting for quantum machines to become powerful enough to break existing systems could be the wrong moment to start defending against them, because the data worth stealing may already have been collected.
As Médard puts it, “there’s nothing quantum about post-quantum security. It’s all coding. Pen-and-paper math.”
The premise behind a quantum-proof blockchain is therefore surprisingly old-fashioned. The machines may be futuristic, but the defense can begin with mathematics that already exists.