An AI model has helped prove a long-standing mathematical conjecture that rules out a quantum advantage for a broad class of problems. The result narrows where researchers should look for quantum speed-ups, but it does not make quantum computers pointless.
Watch Desk analysis
What happened
A team at Physical Superintelligence used Anthropic’s Fable 5.2 to prove the Aaronson-Ambainis conjecture, first formally proposed in 2011. It concerns yes-or-no questions about very long random strings of bits: for most such strings, a conventional computer can approximate a quantum computer’s performance without taking much longer.
New Scientist reports that Fable took about four days to produce, refine twice and verify the proof with the Lean proof-checking software. Researchers at Stanford studied the proof and wrote an exposition of its key ideas. Read the New Scientist report.
Why it matters
The conjecture’s proof rules out one possible route to quantum speed-ups. That is a meaningful result for quantum-computing research: it helps distinguish problems where quantum machines may have an edge from a large class where they probably do not.
The boundary matters. This is a result about a particular kind of problem, not a verdict on quantum computing as a whole. The report notes that factoring, molecular simulation and some optimisation problems remain among the areas where quantum approaches may still be useful.
Our read
The impressive part is not simply that an AI system produced a proof, but that the work was checked with Lean and then examined by mathematicians. That makes this more than a convincing-looking answer in a chat window, though the underlying result and its implications should still be understood within their stated mathematical scope.
For AI-assisted research, the more interesting question may be what happens next: can these tools help researchers tackle ambitious new questions, rather than only settle famous old ones?
What to watch
- Whether the proof and its Lean verification become available for wider scrutiny.
- What mathematical techniques researchers take from the proof and reuse.
- Whether AI-assisted work helps identify new problems with genuine quantum advantage.
Discussion spark: Should researchers now prioritise finding new problems where quantum computers have an advantage, or first test how reliably AI can help prove results like this?
Sources and evidence
- Source update (12 November 2026, 20:00 UTC)
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