In other words, gradient descent isn't good at combinatorial optimisation. I'm sure the research is better but the hype in the blog post leaves a bad taste.
There must be a version of Rich Sutton’s Bitter Lesson that applies to alternative computing like this, along with all the other exciting specialised hardware we've seen come and go over the years, like expert systems, optical computing, neuromorphic computing, etc.
Something like:
General purpose commodity silicon with rapidly evolving software generally beats specialised hardware.
Software is just so much faster to iterate and improve than hardware. AI is also improving it too (eg AlphaEvolve).
Specialized hardware may give a single, significant improvement that grabs headlines but in the long term, compounding small improvements win.
I don't think they are even referring to gradient descent here. I think they are referring to systems like AlphaEvolve where they use LLMs to give an informed/heuristical guess to try to tackle an otherwise insurmountable search space.
> [...] quantum-inspired computing built on CMOS technology [...]
So at the heart of the solution is some FPGA that does something (close to?) quantum computing and that helps exploring exponential search space in somewhat feasible way? Is the gist that we might have stumbled upon a practical application of QC? And if so, what's the secret sauce if not lots of qbits? A new algorithm? Is it just hype?
Can someone that understands quantum computing please comment?
So this isn't quantum computing in the qubit sense, but instead a different computer architecture (demonstrated on an FPGA) that's based on Fowler–Nordheim (FN) quantum tunneling (a real physical effect, used in flash memory, but simulated here).
The paper says:
> The FN-dynamics may be realized either by a physical FN-tunneling device or via a digital emulation of the FN-tunneling dynamical systems. In this work, we employ the digital emulation to achieve the precision required for simulated annealing in the low-temperature regime.
In my opinion this result is quite neat, as it implies this will only get faster/better with "real" (read: analog) hardware.
This is not quantum computing - "quantum-inspired" could just as well be used to describe a process like simulated annealing. The problem they are solving here is a problem often used as a benchmark for quantum computing, but the approach is purely classical.
This is not especially related to quantum computing. Neuromorphic computing uses an algorithm that tries to replicate how the brain works and then in this case implements it and runs it on an FPGA. There are quite a range of papers on this concept and multiple companies are doing just this to show their work. It is often used as it should theoretically avoid such a brute force approach.
They have replicated a neuromorphic algorithm (brain like) on a FPGA, but this implementation at this scale is doubtful to have any improvement over a brute force effort. Quite a few companies feel this is the way forward, although the end goal would be potentially better using photonic chips than qubits and obviously better than an fpga.
The title is especially buzzword based with minimal meaning for the actual paper.
Yes, I actually believe that if we really want to build AI and physical AI, we need this. I'm working on this for a while. vantar.xyz
> Explores what AI cannot
In other words, gradient descent isn't good at combinatorial optimisation. I'm sure the research is better but the hype in the blog post leaves a bad taste.
There must be a version of Rich Sutton’s Bitter Lesson that applies to alternative computing like this, along with all the other exciting specialised hardware we've seen come and go over the years, like expert systems, optical computing, neuromorphic computing, etc.
Something like:
Software is just so much faster to iterate and improve than hardware. AI is also improving it too (eg AlphaEvolve).Specialized hardware may give a single, significant improvement that grabs headlines but in the long term, compounding small improvements win.
I don't think they are even referring to gradient descent here. I think they are referring to systems like AlphaEvolve where they use LLMs to give an informed/heuristical guess to try to tackle an otherwise insurmountable search space.
> [...] quantum-inspired computing built on CMOS technology [...]
So at the heart of the solution is some FPGA that does something (close to?) quantum computing and that helps exploring exponential search space in somewhat feasible way? Is the gist that we might have stumbled upon a practical application of QC? And if so, what's the secret sauce if not lots of qbits? A new algorithm? Is it just hype?
Can someone that understands quantum computing please comment?
So this isn't quantum computing in the qubit sense, but instead a different computer architecture (demonstrated on an FPGA) that's based on Fowler–Nordheim (FN) quantum tunneling (a real physical effect, used in flash memory, but simulated here).
The paper says:
> The FN-dynamics may be realized either by a physical FN-tunneling device or via a digital emulation of the FN-tunneling dynamical systems. In this work, we employ the digital emulation to achieve the precision required for simulated annealing in the low-temperature regime.
In my opinion this result is quite neat, as it implies this will only get faster/better with "real" (read: analog) hardware.
This is not quantum computing - "quantum-inspired" could just as well be used to describe a process like simulated annealing. The problem they are solving here is a problem often used as a benchmark for quantum computing, but the approach is purely classical.
No it's just analogies. It's a normal FPGA.
This is not especially related to quantum computing. Neuromorphic computing uses an algorithm that tries to replicate how the brain works and then in this case implements it and runs it on an FPGA. There are quite a range of papers on this concept and multiple companies are doing just this to show their work. It is often used as it should theoretically avoid such a brute force approach.
> Can someone that understands quantum computing please comment?
...
Crickets
...
Higher-order neuromorphic Ising machines—autoencoders and Fowler-Nordheim annealers are all you need for scalability[0]
[0]https://www.nature.com/articles/s41467-026-71937-4
OK, this is just ridiculous now. Cut it with all this "all you need" crap.
I'm only commenting on the title. I like their work.
This isn’t even a research paper.
Is there some code or results from experiments where we can see the speed up?
Paper is linked on the page (doi.org link redirects to Nature), code here[0]
[0]https://github.com/aimlab-wustl/NeuroSA-HO
So many ... words... big words ...
Can't compute.
Help.
They have replicated a neuromorphic algorithm (brain like) on a FPGA, but this implementation at this scale is doubtful to have any improvement over a brute force effort. Quite a few companies feel this is the way forward, although the end goal would be potentially better using photonic chips than qubits and obviously better than an fpga.
The title is especially buzzword based with minimal meaning for the actual paper.
This reads like the paper from the Sokal affair.