This builds on the Kickmix quantum language from Google. Essentially, Kickmix is an assembly language for classically simulatable quantum circuits, and finding more efficient circuits is basically a game of code golf with the assembly instructions. That means LLMs benefit from the closed loop of verification, and can rapidly test and discard ideas. It's not too surprising that previous academics didn't try to optimize to this level, and a good task to automate. See ecdsa.fail for another example of circuit optimization using the Kickmix language.
It's a nice illustration of the Envelope Principle: AI is good at filling in the blanks, where the knowledge is 'within the envelope' of things already discovered but the area itself is still not fully exhausted. Real progress happens by expanding the area of the envelope, not by coloring in the blanks.
People (most people?) have spent their entire decades long careers coloring in the blanks within the envelope of human knowledge. I think it’s kind of shitty to suggest that they weren’t making “real” progress with their lives, just because AI can now do the same thing.
"This work was led by Andi Gu as part of Anthropic’s STEM Fellows Program, with mentorship from Alek Kemeny, a quantum physicist at Anthropic, in collaboration with Susanne Yelin (Harvard University) and Mikhail Lukin (Harvard University and QuEra Computing). Read the full paper on arXiv."
"We thank Pablo Bonilla, Arjun Kar, Nazli Ugur
Koyluoglu , Rohan Mehta, Varun Menon, and Vinay Ra-
masesh for feedback on the manuscript. AG conducted
part of this work under the Anthropic STEM Fellows
Program. Work at Harvard was supported by IARPA
and the Army Research Office under the Entangled Log-
ical Qubits program (Cooperative Agreement Number
W911NF-23-2-0219) and by the National Science Foun-
dation (NVQL grant PHY-2410716)."
The whole Anthropic commercial appears to be hallucinated.
TLDRAIBBQ: Claude redesigned eight simulated quantum circuits, reducing operations by up to 17× and 12× by reconstructing temporary data and replacing an algorithm with a better one.
This builds on the Kickmix quantum language from Google. Essentially, Kickmix is an assembly language for classically simulatable quantum circuits, and finding more efficient circuits is basically a game of code golf with the assembly instructions. That means LLMs benefit from the closed loop of verification, and can rapidly test and discard ideas. It's not too surprising that previous academics didn't try to optimize to this level, and a good task to automate. See ecdsa.fail for another example of circuit optimization using the Kickmix language.
It's a nice illustration of the Envelope Principle: AI is good at filling in the blanks, where the knowledge is 'within the envelope' of things already discovered but the area itself is still not fully exhausted. Real progress happens by expanding the area of the envelope, not by coloring in the blanks.
People (most people?) have spent their entire decades long careers coloring in the blanks within the envelope of human knowledge. I think it’s kind of shitty to suggest that they weren’t making “real” progress with their lives, just because AI can now do the same thing.
>> Real progress happens by expanding the area of the envelope, not by coloring in the blanks.
This is a pretty shitty thing to say, and if you think about it for even a few minutes, you'll understand why.
I'm normally more of an AI hater but this just seems objectively good so I'll ignore it and move on
This is definitely a level of universal paper clips we’re on…
This is the level where the AI develops quantum computing to make itself smarter (in the game)
Liar Claude:
"This work was led by Andi Gu as part of Anthropic’s STEM Fellows Program, with mentorship from Alek Kemeny, a quantum physicist at Anthropic, in collaboration with Susanne Yelin (Harvard University) and Mikhail Lukin (Harvard University and QuEra Computing). Read the full paper on arXiv."
The actual paper (https://arxiv.org/pdf/2610.22367):
"We thank Pablo Bonilla, Arjun Kar, Nazli Ugur Koyluoglu , Rohan Mehta, Varun Menon, and Vinay Ra- masesh for feedback on the manuscript. AG conducted part of this work under the Anthropic STEM Fellows Program. Work at Harvard was supported by IARPA and the Army Research Office under the Entangled Log- ical Qubits program (Cooperative Agreement Number W911NF-23-2-0219) and by the National Science Foun- dation (NVQL grant PHY-2410716)."
The whole Anthropic commercial appears to be hallucinated.
TLDRAIBBQ: Claude redesigned eight simulated quantum circuits, reducing operations by up to 17× and 12× by reconstructing temporary data and replacing an algorithm with a better one.
What an acronym