Anthony Leverrier @letonyo
researcher on quantum error correction https://t.co/RvavYrCGWD arxiv.org/a/leverrier_a_… Paris Joined May 2009-
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It's very human to only double check that a process is working when you get a weird result. It's also very bad practice, because sometimes your "right" result is due to a bad process and you will be misled. Social scientists (economists) do this kind of asymmetric checking.
We now have the first accuracy results from the largest-ever existential risk forecasting tournament. In 2022, we convened 80 experts and 89 superforecasters for the Existential Risk Persuasion Tournament (XPT), which collected thousands of forecasts in 172 questions across…
So we went from "LLM is memorizing dataset" to "LLM is not reasoning" to "LLM cannot do long / complex math proving" to "Math that LLM is doing is not REAL math. LLM can't do REAL math" Where do we go from now?
So we went from "LLM is memorizing dataset" to "LLM is not reasoning" to "LLM cannot do long / complex math proving" to "Math that LLM is doing is not REAL math. LLM can't do REAL math" Where do we go from now?
This is really exciting and impressive, and this stuff is in my area of mathematics research (convex optimization). I have a nuanced take. 🧵 (1/9)
This is really exciting and impressive, and this stuff is in my area of mathematics research (convex optimization). I have a nuanced take. 🧵 (1/9)
Claim: gpt-5-pro can prove new interesting mathematics. Proof: I took a convex optimization paper with a clean open problem in it and asked gpt-5-pro to work on it. It proved a better bound than what is in the paper, and I checked the proof it's correct. Details below.
ha! you meant *those* nails 🤦
ha! you meant *those* nails 🤦
GPT-5 didn’t live up to OpenAI’s hype, but it is *exactly* in line with extrapolations from prior AI advancements. Go ahead and discount future statements from OpenAI/Altman, but you should still expect the fast AI progress that we’ve been seeing to continue
might apply to quantum computing startups as well...
might apply to quantum computing startups as well...
@letonyo Relates to the fact that the strength of the architecture is also a weakness--data and instructions stored in the same memory. CPU is fast so has to wait around for data. en.wikipedia.org/wiki/Von_Neuma…
Great work by Yiyi Cai that improved the analysis of robustness for the local codes used in quantum Tanner codes. (To be fair, the bounds remain quite pessimistic if one wishes good quantum LDPC codes with linear distance.) thesis.library.caltech.edu/17412/
Great work by Yiyi Cai that improved the analysis of robustness for the local codes used in quantum Tanner codes. (To be fair, the bounds remain quite pessimistic if one wishes good quantum LDPC codes with linear distance.) thesis.library.caltech.edu/17412/
also, here is one part that people not interested in the rest of the post might still be interested in:
What a great initiative !
#newpaper Bosonic codes can dramatically reduce the cost of fault tolerance and many codes look like serious contenders: dual-rail qubit, cat qubit, GKP. But the Fock space is huge, esp. if you consider multimode encodings, and there are many great codes waiting to be found (1/4)
I'm often asked if I'll redo the 2019 quantum factoring estimate. Denser storage by yokes, smaller magic factories by cultivation, slimmer approx arithmetic by Chevignard et al… surely the cost is lower now? Yes, it's lower now. security.googleblog.com/2025/05/tracki… arxiv.org/abs/2505.15917
is there a standard name for the representation of U(n) on the n-mode Fock space that associate to U \in U(n) the unitary {a_i} --> {\sum_j U_{i,j} a_j} ? "metaplectic representation"?

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