AI has dramatically accelerated the quantum threat to Bitcoin: AI Eye

Artificial intelligence is speeding up the pace of research into quantum computers.

Last week, the estimated timeline for Q Day — the date a cryptographically relevant computer is able to break encryption — grew significantly shorter thanks to research papers from Google and the Caltech-linked quantum startup Oratomic.

Google got all the attention, but the Oratomic paper was even more significant and suggested that a quantum computer with just 10,000 neutral atom qubits could break Bitcoin’s ECDSA algorithm using an optimized version of Shor’s algorithm. That’s ten times fewer than The Pinnacle Architecture’s groundbreaking estimate in February (100,000 physical qubits), which itself was ten times fewer than Google’s Craig Gidney estimated last year.

With 10,000 qubits, it might take a couple of hundred days to crack, but attackers could get their hands on at least one of Satoshi’s wallets. To put that in perspective, Caltech has already built a quantum computer array with 6,000 neutral atom qubits. They still need to solve error correction and some other engineering challenges, but a 10,000-qubit machine able to crack Bitcoin no longer seems like science fiction.

Cloudflare announced in the past week it was “accelerating” its deadline to prepare for quantum computers to 2029 — and it’s already more prepared than most blockchains. Google did the same a fortnight ago.

The research paper didn’t mention it, but it’s now emerged that AI was instrumental in developing and refining Oratomic’s Bitcoin cracking tech. “There is no question that we used AI to accelerate this development,” Oratomic’s Dolev Bluvstein told TIME. “No question at all.”

The algorithm the team developed initially was “about 1000 times worse” according to paper co-author Robert Huang, who previously worked at Google Quantum AI. That is, until they feed it into the open-source AI tool OpenEvolve to optimize it.

The system considered many thousands of ideas and approaches, and narrowed them down using a natural selection-like process. Normally, it takes 100 to 1,000 atoms to encode a single qubit, but the AI helped come up with an algorithm that required just three.

That reduces the number of particles required to build an atomic quantum computer by 100 times. 

The Oratomic team plans to publish a follow-up paper detailing how the AI sped up the process. 

Google Quantum AI and DeepMind have been using AI to research quantum error correction for a number of years, which produced the AlphaQubit AI system in 2024.  

The company is also developing its own neutral atom quantum computer, helped along by AI-based “discovery pipelines.”  

AI systems have also helped discover better materials for physical qubits. 

Research published in Nature in December suggested that one reason AI is so helpful with quantum computing breakthroughs is that quantum mechanics is difficult for humans to understand, as it contradicts our experiences with classic physics. 

“The counterintuitive nature and high-dimensional mathematics of QC make it a prime candidate for AI’s data-driven learning capabilities, and in fact, many of QC’s biggest scaling challenges may ultimately rest on developments in AI,” the paper said.  

Oratomic is racing to build the world’s first quantum computer. “The world is currently, in my view, not prepared,” Bluvstein said.

Also read: All 21 million Bitcoin is at risk from quantum computers

Why waste time say lot word when few word do trick?

LLMs never say anything in ten words that they could express in 1,000 words.

All of that empty, verbose content costs users loads of tokens to generate. Every time it replies to another query, it rereads all the other essays it wrote again, which costs more tokens.

Redditorffatty was experimenting with trying to make Claude more concise when he hit upon the idea of making Claude talk like a caveman as a gag. But he says he found it used 75% less tokens, giving the examples:

“I executed the web search tool” = 8 tokens

Caveman version: “Tool work” = 2 tokens

Subreddit members suggested you could design a prompt to do something similar by writing: “Short sentences only (3-6 words),” “No filler/preamble,” and “Tool first, result first, no explain.”

They also pointed out that…