Realized how to frame my question as an optimization problem to apply gradient descent.
It's all downhill from here.
09.02.2026 04:44
👍 37
🔁 3
💬 3
📌 1
Realized how to frame my question as an optimization problem to apply gradient descent.
It's all downhill from here.
Preprint, by Clément Canonne, Kenny Chen, and Julian Mestre: "The Quantumly Fast and the Classically Forrious", February 7, 2026
Teaser: coming soon to an #arXiv near you!
(my coauthors did not change the title in time)
Glad to meet a fellow pundit
![Many of the potential applications of Hamiltonian simulation are not algorithms themselves, but
rather, the idea that having a better understanding of certain physical or chemical systems would likely
lead to new scientific and technological breakthroughs. Some of these are well worked out ideas (for
example, nitrogen fixation [RWS+17]), but many of them are very tenuous, which unfortunately does
not stop popular science news, and technology-enthusiasts who have decided to make a career talking
a lot about quantum computing without really understanding it, from treating such applications as
being just around the corner. You will find headlines and ted talks claiming quantum computers can
solve every futuristic-sounding problem, including fixing climate change [mtl], curing cancer [Kak24],
and finding the secret to immortality [Gre20]. I mean, science could solve any of these (but could it?),
and faster Hamiltonian simulation would mean we can do better science, so. . . The reality is, there
probably will be many applications to being able to simulate physical systems, but we do not yet know
what they will be. We will not discuss applications of Hamiltonian simulation in this course, but it
is important to understand that there is a lot of hype around quantum computing, some of which is
justified, and some of which is not.](https://cdn.bsky.app/img/feed_thumbnail/plain/did:plc:tepknacn7qtpcddry35b3g2x/bafkreibcs6y26budwxemzcdm4sxf4ltootd5f76rwvr4efnbebvrc2nm3a)
Many of the potential applications of Hamiltonian simulation are not algorithms themselves, but rather, the idea that having a better understanding of certain physical or chemical systems would likely lead to new scientific and technological breakthroughs. Some of these are well worked out ideas (for example, nitrogen fixation [RWS+17]), but many of them are very tenuous, which unfortunately does not stop popular science news, and technology-enthusiasts who have decided to make a career talking a lot about quantum computing without really understanding it, from treating such applications as being just around the corner. You will find headlines and ted talks claiming quantum computers can solve every futuristic-sounding problem, including fixing climate change [mtl], curing cancer [Kak24], and finding the secret to immortality [Gre20]. I mean, science could solve any of these (but could it?), and faster Hamiltonian simulation would mean we can do better science, so. . . The reality is, there probably will be many applications to being able to simulate physical systems, but we do not yet know what they will be. We will not discuss applications of Hamiltonian simulation in this course, but it is important to understand that there is a lot of hype around quantum computing, some of which is justified, and some of which is not.
Just LOVE the "Advanced Quantum Algorithms" lecture notes of Stacey Jeffery!!!
homepages.cwi.nl/~jeffery/not...
Everyone teaching quantum computing should read this paragraph out loud to their students!
homepages.cwi.nl/~jeffery/not...
Screenshot of the YouTube playlist for the course.
On the topic of online resources, worth spreading the word again about Ryan O'Donnell's "CS Theory Toolkit" course: "Covers a large number of the math/CS topics that you need to know for reading and doing research in Computer Science Theory"
youtube.com/playlist?lis... @booleananalysis.bsky.social
I just gave a tutorial on Design Templates for Dynamic Graph Algorithms at IISc in Bangalore.
The kindest words I received were "best tutorial I have listened to in the last 10 years." Hope it interests you.
Video: www.youtube.com/live/L8ev24g...
Slides: tinyurl.com/yetx3vxu
How should I read this?
There is some illuminatingchatter on the lean Zulip about this. Including the authors.
I am on an infinite ad loop with the link. Is it just me?
Open Printer: an open-source inkjet printer with no DRM, no subscriptions, and repairable parts.
Sometimes the best innovation is just making things work the way they should have all along.
Today at IAS, I gave a 2 hr 15 mins lecture on why TIME[t] is in SPACE[√(t log t)]. You can watch it here!
www.youtube.com/watch?v=ThLv...
It would have to be faster in GPU hardware and they would have to be able to budget for it, given how expensive GPUs are at scale.
I recall that there is some research into sparsifying neural network weights as well. This sort of speed up might be cheaper.
You likely have a world model running inside your skull right now — it’s how you know not to step in front of a moving train without needing to run the experiment first. So far, AI lacks such a scaled-down representation of the environment.
An illustration of a man falling out of a piece of paper, with text that says: How an academic betrayal led me to change my authorship practices.
"The day the paper was published should have been a moment of pride. Instead, it felt like a quiet erasure." #ScienceWorkingLife https://scim.ag/4p3eH5g
Maxwell’s equations were perfectly sufficient for radios.
I think I recognise Indian English there. “I’d love a pointer” is Indian corporate-speak.
To all the people mocking LLMs because it's "just matrix multiplication (linear algebra):"
Careful there! You're going to piss off the quantum physicists
Thorsten Altenkirch explains Gödel's Incompleteness Theorem on @computerphile.bsky.social, and shows some definitions in #LeanLang! 🎯
Watch here: www.youtube.com/watch?v=IuX8...
After 3 1/2 years of work my course on quantum computing is finally finished — the "Director's Cut" of Understanding Quantum Information and Computation is now available.
arxiv.org/abs/2507.11536
Table of contents of the monograph
Reminder/plug: my graduate-level monograph on "Topics and Techniques in Distribution Testing" (FnT Comm. and Inf Theory, 2022).
📖 ccanonne.github.io/survey-topic... [Latest draft+exercise solns, free]
📗 nowpublishers.com/article/Deta... [Official pub]
📝 github.com/ccanonne/sur... [LaTeX source]
This lecture provides a gentle introduction to amortized analysis.
For experts: At the end, I explained Hollow Heaps, an optimal heap like Fibonacci heaps, but simpler! Surprisingly, I have not seen video lectures on this before.
www.youtube.com/watch?v=8mHa...
I must note that it would be harder to check the correctness of definitions and theorems in such an autoformalisation than review the pen and paper version in a conference.
This is not to say that they can’t get better at this stuff, but the bigger the formalisation effort, the less one should trust an AI with the definitions and theorem statements. And even after ensuring correctness, such a formalisation would be devoid of all the uses one could put it to.
In general there is a wide gap between what ML researchers achieve in experiments and how well it works in practice. In addition, there is a deployment gap. In our equational theories project we explicitly noted that modern AI was extremely underwhelming.
Yes. This was discussed in the lean Zulip. I don’t take it too seriously for the following reason. This tool was provided a very fine grained blueprint. I can imagine that for the typical SODA paper, even with all the prerequisite material existing in some library, it would be a 1000 pages.
I am inclined to believe that AI tools will have the same effect on formal proofs. They might take users to intractable proof states and give up when used or modify definitions incorrectly to get proofs accepted.
Markus Himmel has written a blog post about how to write a simple imperative program in Lean and then how to verify that the program is bug-free.
markushimmel.de/blog/my-firs...
📣 We're excited to share the new lean-lang.org!
Relaunching our website was a key deliverable in our Year 2 roadmap to provide "improved navigation and access to valuable content, resources, and tools." We hope you like it!
#LeanLang #LeanProver
I finally understand the mechanism of the Jacquard loom. Still cannot fathom how designers program the holes.
youtu.be/pzYucg3Tmho
