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keeganryan 16 hours ago [-]
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.
jacquesm 16 hours ago [-]
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.
giovannibonetti 8 hours ago [-]
> 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.
That is a very interesting example but it serves very much to illustrate my point: this is latent knowledge, we effectively already have it but we just haven't put 2 and 2 together. Unfortunately AI excels at such problems because it oversees a much larger slice of our knowledge than most individual humans. I've always said that cross domain application of knowledge is the easiest way to make progress and I haven't really seen anything conflicting with that to date.
So while you start off with 'disagreement' in fact it appears we agree far more than we disagree. Yes, it is important that this work is done. But you clearly illustrate that the envelope did not in fact expand. But all the time scientists around the world do in fact expand the envelope of our knowledge and one of my fears is that by undercutting a lot of such knowledge work by putting this digital combine to work on it that we will lose the infrastructure to do the rest. We'll see a lot of progress in a very short time and then we'll find out that because we took away that class of problems that it has become almost impossible to keep the infrastructure, incentive structure and educational paths in place to be able to do the next round.
You see a similar thing happening in software development. All of a sudden it has become a lot harder to start as a career. And lots of people are not even going to try this direction if there is no first rung on the ladder.
ssivark 4 hours ago [-]
> Unfortunately AI excels at such problems because it oversees a much larger slice of our knowledge than most individual humans. I've always said that cross domain application of knowledge is the easiest way to make progress and I haven't really seen anything conflicting with that to date.
What is unfortunate about this? I suspect we concur in not buying into the AGI/ASI/RSI/<jargon of the day> hype, but this isn't this kind of breakthrough extremely valuable regardless?
Maybe it's not expanding the convex hull of what is possible, but the fact is that human progress is extremely jagged even within that convex hull -- for myriad reasons limitations related to funding, cross-domain expertise, number of scientists, etc.
----
If you're worried about what it takes for a novice setting out in the field, it might be useful to see how specific "mature" subfields have evolved in their training structures. Eg. I have a background in theoretical physics and it is common for grad students to do courses for a couple of more years and take a few more years to reach the same productivity compared to grad students in (say) CS/AI.
red75prime 5 hours ago [-]
At this point, it’s better to ponder a question: Are there fundamental reasons why a search trajectory during reinforcement learning can’t leave the envelope? And: what is the envelope, anyway? A metaphorical surface in a metaphorical space of ideas with no defined procedures to make it into an actual metric space?
ssivark 4 hours ago [-]
Reinforcement learning can definitely (in principle) leave this envelope, but it depends crucially on whether the feedback generates new information from reality, rather than just repackaging what humans have already discovered in the form of RL environments and densified rewards. So the question is not about RL per se, but what is the information source, what is the bandwidth of information it supplies, and then only finally the effectiveness of RL in distilling information that into models.
derektank 14 hours ago [-]
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.
jacquesm 14 hours ago [-]
Of course it is progress. But there are orders here: filling in the blanks is super useful and for sure you can - or should I say could - make a living with that. But there is another level above that which is where the total area is increased. That's not meant as a sleight against people that were ensuring all the ground was covered, it's just establishing the fact that there are multiple levels to knowledge.
If you want to make it seem as if I'm pissing on people that were doing useful science that's your privilege, but there is a reason we use terms like 'groundbreaking' about discoveries that are different.
As for lost careers: I've been schlepping bits my whole life, and probably an RTX 6000 could re-run that in under a year, and no doubt what I did was of less significance than that done by the lowliest of scientists. Still, I don't derive my sense of self value from what future technology can do any more than I think that a day laborer should compare themselves with a tractor or a combine.
Karrot_Kream 13 hours ago [-]
I'm not sure of your background, but FWIW everyone in my PhD program in my elite CS program had varying levels of anguish and angst when realizing that their efforts were mostly suited for "coloring in the blanks." The PhD Grind [1] felt like a poignant capture of those feelings.
>> 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.
jacquesm 14 hours ago [-]
You are just echoing the words of the previous commenter in this thread. For my answer, please see there to avoid repetition.
gazarsgo 13 hours ago [-]
"Two people noticed the same thing and I didn't learn after hearing it twice."
jacquesm 13 hours ago [-]
Thank you for your 10th contribution to HN in a decade. I'm happy I was able to motivate you to participate, please do it more often.
mrmuagi 13 hours ago [-]
Well it depends if commenting on HN is considered "within the envelope", maybe it's not real progress worth spending time on :)
ashkankiani 11 hours ago [-]
Legitimately true. This is a forum for entertainment and reposting content, not really learning anything seriously.
jacquesm 12 hours ago [-]
That's a fact.
red75prime 15 hours ago [-]
[flagged]
cmrx64 4 hours ago [-]
(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.
adastra22 10 hours ago [-]
> 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…
perching_aix 7 hours ago [-]
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.
adastra22 5 hours ago [-]
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.
dr_dshiv 6 hours ago [-]
Quantum Vibe Coding: anyone can do it!
mariusor 3 hours ago [-]
When one computing discipline where human mind can't comprehend how it works is not enough...
kingcauchy 16 hours ago [-]
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)
Jtarii 12 hours ago [-]
ASI is coming long before usable quantum computers are.
adastra22 10 hours ago [-]
This is ASI, so yes.
cgio 7 hours ago [-]
Weird they don’t mention Bennet (1973)?
JacobAsmuth 15 hours ago [-]
I'm normally more of an AI hater but this just seems objectively good so I'll ignore it and move on
whoishiding 17 hours ago [-]
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.
mixtureoftakes 16 hours ago [-]
What an acronym
12923817 16 hours ago [-]
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."
"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.
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
So while you start off with 'disagreement' in fact it appears we agree far more than we disagree. Yes, it is important that this work is done. But you clearly illustrate that the envelope did not in fact expand. But all the time scientists around the world do in fact expand the envelope of our knowledge and one of my fears is that by undercutting a lot of such knowledge work by putting this digital combine to work on it that we will lose the infrastructure to do the rest. We'll see a lot of progress in a very short time and then we'll find out that because we took away that class of problems that it has become almost impossible to keep the infrastructure, incentive structure and educational paths in place to be able to do the next round.
You see a similar thing happening in software development. All of a sudden it has become a lot harder to start as a career. And lots of people are not even going to try this direction if there is no first rung on the ladder.
What is unfortunate about this? I suspect we concur in not buying into the AGI/ASI/RSI/<jargon of the day> hype, but this isn't this kind of breakthrough extremely valuable regardless?
Maybe it's not expanding the convex hull of what is possible, but the fact is that human progress is extremely jagged even within that convex hull -- for myriad reasons limitations related to funding, cross-domain expertise, number of scientists, etc.
----
If you're worried about what it takes for a novice setting out in the field, it might be useful to see how specific "mature" subfields have evolved in their training structures. Eg. I have a background in theoretical physics and it is common for grad students to do courses for a couple of more years and take a few more years to reach the same productivity compared to grad students in (say) CS/AI.
If you want to make it seem as if I'm pissing on people that were doing useful science that's your privilege, but there is a reason we use terms like 'groundbreaking' about discoveries that are different.
As for lost careers: I've been schlepping bits my whole life, and probably an RTX 6000 could re-run that in under a year, and no doubt what I did was of less significance than that done by the lowliest of scientists. Still, I don't derive my sense of self value from what future technology can do any more than I think that a day laborer should compare themselves with a tractor or a combine.
[1]: https://dokumen.pub/the-phd-grind-a-phd-student-memoir-3.htm...
This is a pretty shitty thing to say, and if you think about it for even a few minutes, you'll understand why.
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.
So it’s not performing the same computation…
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.
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.
This is the level where the AI develops quantum computing to make itself smarter (in the game)
"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.