AI model helps settle a long-open quantum computing question
The result rules out one route quantum machines could take on some hard problems, though verification is still informal and not all routes are closed.
A startup lab called Physical Superintelligence says its member David Turturean used early access to Anthropic's Fable 5.2 model to address the Aaronson-Ambainis conjecture, a long-open question about when quantum computers gain speed over classical ones. Scott Aaronson says he does not fully follow the proof but believes it is sound, having seen it autoformalized in Lean and explained by outside researchers the lab shared it with. The result seems to rule out one route quantum machines could take on certain hard problems, though not all of them. The lab currently earns revenue from simulating data center physics, and its founder says the longer goal is turning theoretical breakthroughs into technology. An academic adviser notes that norms for AI-driven science are still forming, and that a physics summit at MIT is scheduled for next week to discuss the pace of change.
Why it matters
The result narrows what quantum computers might be expected to do faster than classical machines, at least along one path. For those who follow the field, it is a concrete case of an AI system contributing a proof to a question that had stayed open for eighteen years, and it shows how such claims are being checked: through autoformalization in Lean and review by outside researchers, not by the authors alone. Norms for judging AI-driven science are still forming, and a physics summit at MIT next week will discuss how quickly the field is moving.
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