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.
> Real progress happens by expanding the area of the envelope, not by coloring in the blanks.
Respectfully disagree. What one person may classify as expanding the envelope, another one with broader knowledge may classify as coloring in the blanks. Much knowledge in one field comes from cross-pollination from other fields.
Let me give you an example I just learned about. The database community spent decades trying (to some extent) to solve the implementation of materialized views to arbitrary complex SQL queries. Then, a few years ago, someone with experience in digital signal processing brought their expertise to the DB field and completed the puzzle. [1]
Someone with DB expertise but no signal processing expertise might say that is expanding the envelope, but another person with experience in both may say it is coloring in the blanks.
AI work now may seem like coloring in the blanks, but given it's vast expertise in multiple fields, my bet is that for most people it will look like pushing the envelope very soon.
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.
> In other cases, it found that existing circuits included costly components that were needed to handle only extremely rare inputs, and it redesigned them without those components.
Why the ellipses? It's not like they're sweeping this under the rug, it's pretty explicitly stated for which optimizations this applies. It's also a perfectly standard optimization strategy.
Their second example features it for example, but as an additional optimization that saves memory. The compute optimization part doesn't need this, and is what brings the little over an order of magnitude speedup.
We have excellent approximations of the full quantum chemistry that work really well, e.g. DFT. It's the gold standard for chemistry today. But these edge cases, although rare, do show up. They have a nasty tendency of showing up in biology especially, where reactions often happen on a knife's edge of the energy landscape, for efficiency reasons.
Chemists are interested in quantum simulation because it would be fully accurate and be an actual substitute for ground-truth experiment. With these approximations, you still wouldn't know if what you're simulating matches reality.
(warning: pile of AI) I tried autoformalizing my advisor’s decades of papers in https://github.com/emberian/graphplay he studied “Grover-like” problems that evade some of the no-go theorems. these AI are a lot better at connecting domains and ideas for algorithmic insight than we have previously had available (the state of the art being not much better than Llull’s circles). I expect we’ll see a big burst of early progress.
I haven’t started yet, but modeling various diffusion and other operators inside the framework might find some Grover-speedups to some ML primitives of interest.
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 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.
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.
> Real progress happens by expanding the area of the envelope, not by coloring in the blanks.
Respectfully disagree. What one person may classify as expanding the envelope, another one with broader knowledge may classify as coloring in the blanks. Much knowledge in one field comes from cross-pollination from other fields.
Let me give you an example I just learned about. The database community spent decades trying (to some extent) to solve the implementation of materialized views to arbitrary complex SQL queries. Then, a few years ago, someone with experience in digital signal processing brought their expertise to the DB field and completed the puzzle. [1]
Someone with DB expertise but no signal processing expertise might say that is expanding the envelope, but another person with experience in both may say it is coloring in the blanks.
AI work now may seem like coloring in the blanks, but given it's vast expertise in multiple fields, my bet is that for most people it will look like pushing the envelope very soon.
[1] https://arxiv.org/abs/2203.16684
6 replies →
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.
4 replies →
[flagged]
>> 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.
6 replies →
> In other cases, it found that existing circuits included costly components that were needed to handle only extremely rare inputs, and it redesigned them without those components.
So it’s not performing the same computation…
Why the ellipses? It's not like they're sweeping this under the rug, it's pretty explicitly stated for which optimizations this applies. It's also a perfectly standard optimization strategy.
Their second example features it for example, but as an additional optimization that saves memory. The compute optimization part doesn't need this, and is what brings the little over an order of magnitude speedup.
We have excellent approximations of the full quantum chemistry that work really well, e.g. DFT. It's the gold standard for chemistry today. But these edge cases, although rare, do show up. They have a nasty tendency of showing up in biology especially, where reactions often happen on a knife's edge of the energy landscape, for efficiency reasons.
Chemists are interested in quantum simulation because it would be fully accurate and be an actual substitute for ground-truth experiment. With these approximations, you still wouldn't know if what you're simulating matches reality.
1 reply →
(warning: pile of AI) I tried autoformalizing my advisor’s decades of papers in https://github.com/emberian/graphplay he studied “Grover-like” problems that evade some of the no-go theorems. these AI are a lot better at connecting domains and ideas for algorithmic insight than we have previously had available (the state of the art being not much better than Llull’s circles). I expect we’ll see a big burst of early progress.
I haven’t started yet, but modeling various diffusion and other operators inside the framework might find some Grover-speedups to some ML primitives of interest.
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)
ASI is coming long before usable quantum computers are.
This is ASI, so yes.
Quantum Vibe Coding: anyone can do it!
When one computing discipline where human mind can't comprehend how it works is not enough...
I'm normally more of an AI hater but this just seems objectively good so I'll ignore it and move on
Weird they don’t mention Bennet (1973)?
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
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.