Extropic

I know they mainly present results on deep learning/neural network training and optimization, but I wonder how easy it would be to use the same optimization framework for other classes of hard or large optimization problems. I was also curious about this when I saw posts about Extropic (https://www.extropic.ai/) stuff for the first time. I tried looking into any public info on their website about APIs or software stack to see what's possible beyond NN stuff to model other optimization problems. It looks like that's not shared publicly yet. There are certainly many NP-hard and large combinatorial or analytical optimization problems still out there that are worth being able to tackle with new technology. Personally, I care about problems in EDA and semiconductor design. Adiabatic quantum computing was one technology with the promise of solving optimization problems (and quantum computing is still playing out with only small-scale solutions at the moment). Hoping that these new "thermodynamic computing" startups also might provide some cool technology to explore these problems with.

I wonder how this compares to thermal wells? https://extropic.ai They seem to work in a similar way, sampling from chaotic datasets to find the lowest energy state. Is one fundamentally more scalable? More efficient?

does Guillaume Verdon from https://www.extropic.ai/ have thoughts on on cerebras? (or other people that read the litepaper https://www.extropic.ai/future)

Is this what Extropic (https://www.extropic.ai/) is aiming to commercialize and bring to market?

From my understanding, this is exactly what https://extropic.ai is working on, and I wouldn't be surprised if https://normalcomputing.ai/ (authors of the paper) is as well.

interesting point. i know of at least one startup working with that concept[1]. Im sure there are others. 1 - https://www.extropic.ai/

> In their research, the ONISQ team focused on Rydberg qubits – a type of physical, non-logical qubits. Through this effort, they successfully developed techniques to create error-correcting logical qubits from these “noisy” Rydberg qubits. - are these "noisy" in so far as they are only able to approximate absolute zero? At theoretical absolute zero, would there be zero noise? This sounds like a big opportunity for companies like Extropic [1] that are focused on thermodynamic computing. [1] https://www.extropic.ai/

This seems to be the page that describes the low level details of what the hardware aims to do. https://extropic.ai/writing/tsu-101-an-entirely-new-type-of-computing-… To me, the biggest limitation is that you’d need an entirely new stack to support a new paradigm. It doesn’t seem compatible with using existing pretrained models. There’s plenty of ways to have much more efficient paradigms of computation, but it’ll be a long while before any are mature enough to show substantial value.

This one just released a prototype platform, "XTR-0", so if it's a fraud the jig will shortly be up. https://extropic.ai/writing/inside-x0-and-xtr-0

Actually it's not. Here's some stuff to read to get a clearer picture! https://extropic.ai/writing

If you want to understand exactly what we are building, read our blogs and then our paper https://extropic.ai/writing https://arxiv.org/abs/2510.23972